societies that are becoming of keen interest to the speech and
/cavs/hse/ies/www/data/mailing_lists/ies_isip/2005/archive/msg00035.html: in the fall, much older and wiser about speech recognition :)
/cavs/hse/ies/www/data/mailing_lists/ies_isip/2005/archive/msg00089.html: What is a difference in the model for a normal 3-coin model (as discussed in the speech course) and a 3-state left-to-right HMMs?
/cavs/hse/ies/www/data/mailing_lists/ies_isip/2005/archive/msg00090.html:discussed in the speech course) and a 3-state left-to-right HMMs?
/cavs/hse/ies/www/data/mailing_lists/ies_isip/2005/archive/msg00091.html:discussed in the speech course) and a 3-state left-to-right HMMs?
/cavs/hse/ies/www/data/mailing_lists/ies_isip/2005/archive/msg00093.html:discussed in the speech course) and a 3-state left-to-right HMMs?
/cavs/hse/ies/www/data/mailing_lists/ies_isip/2005/archive/msg00118.html:and data structure. Lattices used in speech are actually simple
/cavs/hse/ies/www/data/mailing_lists/ies_weekly/2005/archive/msg00016.html: structure that mirrors the one used for our speech and signal
/cavs/hse/ies/www/data/mailing_lists/ies_weekly/2005/archive/msg00015.html: speech recognition (non-native speakers) and a person who
/cavs/hse/ies/www/data/mailing_lists/ies_weekly/2005/archive/msg00014.html:> goes to the speech website and sees that nothing has been updated
/cavs/hse/ies/www/data/mailing_lists/ies_weekly/2005/archive/msg00010.html:goes to the speech website and sees that nothing has been updated since
/cavs/hse/ies/www/data/mailing_lists/ies_weekly/2005/archive/msg00034.html: o Dr. Picone agreed to be an Associate Editor of a new speech and
/cavs/hse/ies/www/data/mailing_lists/ies_weekly/2005/archive/msg00050.html: in the fall, much older and wiser about speech recognition :)
/cavs/hse/ies/www/data/mailing_lists/ies_weekly/2005/archive/msg00063.html: in an ability to evaluate and optimize speech recognition systems.
/cavs/hse/ies/www/data/mailing_lists/ies_weekly/2005/archive/msg00061.html:My supervisor, John Johnson, will be in MS in the middle of August. He wants to stop by MSU and see our facilities (CAVS). He's excited about starting a speech lab at LLNL, and wants to meet some of the students in IES. He's also interested in hiring post graduates, so if you're looking for a job opportunity, make sure you talk to him. He should let me know the details of his visit pretty soon. I'll update you as soon as I find out.
/cavs/hse/ies/www/data/mailing_lists/ies_weekly/2005/archive/msg00054.html:filter and the input speech data. To do this Sanjay wanted a stream of
/cavs/hse/ies/www/data/mailing_lists/ies_weekly/2005/archive/msg00071.html:and its application in speech enhancement," IEEE transaction on
/cavs/hse/ies/www/data/mailing_lists/ies_weekly/2005/archive/msg00079.html:comments for the data recorder, signal detector and speech recognition
/cavs/hse/ies/www/data/mailing_lists/ies_weekly/2005/archive/msg00131.html:accent to her speech and got some better recognition rates. I imparted
/cavs/hse/ies/www/data/mailing_lists/ies_weekly/2005/archive/msg00123.html:- Understood the basics of CD-HMMs and AR-HMMs, as applied to practical pattern recognition systems (including speech recognition decoders). I have studied the theory of HMMs previously for a pattern recognition course - however the study was restricted to discrete HMMs. A review of the tutorial by L.Rabiner and the lecture notes at
http://www.cavs.msstate.edu/hse/ies/publications/courses/ece_8463/lectures/current/lecture_24/index.html were helpful in gaining a good insight into the fundamental setup of Markov models as speech recognition back-ends. I also read the journal paper: Biing-Hwang Juang, and Lawrence R. Rabiner, "Mixture Auto-Regressive Hidden Markov Models for Speech Signals", IEEE Transactions on Acoustics, Speech, And Signal Processing, Vol. ASSP-33, No. 6, December 1985. It contains an exhaustive explanation of using as basis pdfs 'Auto-Regressive Gaussians', for modeling the state-emission probabilities of observed vectors. An interesting observation made here was that previously, people have attempted to use blocks of raw acoustic data for speech recognition using HMMs. Dr. Picone explained how the idea of AR-HMMs can be extended to incorporate nonlinear time-series modeling, allowing for behaviors such as strange attractors - I can see myself taking a Chaos Theory DIS sometime in the near future :)
/cavs/hse/ies/www/data/mailing_lists/ies_weekly/2005/archive/msg00149.html: -Read (along with Sanjay) papers on determination of Lyapunov exponents from a time-series. Lyapunov exponents are one way to characterize chaotic systems (whether they are deterministic or stochastic). Theoretically, they model the evolution of orbits in a d-dimensional phase-space for a set of differential equations. The problem is to construct the orbit in the phase-space when only one-dimensional data (say speech signal) is available from physical measurements and then to extract the Lyapunov parameters.
/cavs/hse/ies/www/data/mailing_lists/ies_weekly/2005/archive/msg00147.html:- Documented all the client programs. I had finished documenting the speech analysis client last week. Following that I documented the speech recognition, dialog system and the speaker verification client. I just finished this an hour ago. I don't want to check in all these code without one or two rounds of testing. I will check it in this weekend.
/cavs/hse/ies/www/data/mailing_lists/ies/2005/archive/msg00150.html:> Viterbi, and all that good stuff and using the speech course on our
/cavs/hse/ies/www/data/mailing_lists/ies/2005/archive/msg00149.html:Viterbi, and all that good stuff and using the speech course on our web
/cavs/hse/ies/www/data/mailing_lists/ies/2005/archive/msg00145.html: acoustic modeling in speech recognition. On Thursday and Friday,
/cavs/hse/ies/www/data/mailing_lists/ies/2005/archive/msg00107.html:entire speech file to the GPU at once, and the cool thing is each window
/cavs/hse/ies/www/data/mailing_lists/ies/2005/archive/msg00038.html:with GPU's and using them for speech recognition applications. GPUs are
/cavs/hse/ies/www/data/mailing_lists/ies/2005/archive/msg00037.html:with GPU's and using them for speech recognition applications. GPUs are
/cavs/hse/ies/www/data/mailing_lists/ies/2005/archive/msg00021.html:Automatic speech recognizers perform poorly when training and test data
/cavs/hse/ies/www/data/mailing_lists/ies/2005/archive/msg00021.html:Consequently, both automatic speech recognition and automatic speaker
/cavs/hse/ies/www/data/mailing_lists/ies/2005/archive/msg00021.html:and statistical signal processing for robust speech recognition and
/cavs/hse/ies/www/data/mailing_lists/ies/2005/archive/msg00011.html:speech analysis, speech recognition, speaker verification and dialog
/cavs/hse/ies/www/data/mailing_lists/ies/2005/archive/msg00001.html:Automatic speech recognizers perform poorly when training and test data
/cavs/hse/ies/www/data/mailing_lists/ies/2005/archive/msg00001.html:Consequently, both automatic speech recognition and automatic speaker
/cavs/hse/ies/www/data/mailing_lists/ies/2005/archive/msg00001.html:and statistical signal processing for robust speech recognition and
/cavs/hse/ies/www/data/mailing_lists/ies/2005/archive/msg00208.html:and if you believe that, I'd like to sell you a speech recognition
/cavs/hse/ies/www/data/mailing_lists/ies/2005/archive/msg00207.html:and if you believe that, I'd like to sell you a speech recognition
/cavs/hse/ies/www/data/mailing_lists/ies/2005/archive/msg00206.html:and if you believe that, I'd like to sell you a speech recognition
/cavs/hse/ies/www/data/mailing_lists/ies/2005/archive/msg00205.html:and if you believe that, I'd like to sell you a speech recognition
/cavs/hse/ies/www/data/mailing_lists/ies/2005/archive/msg00221.html:speaker verification and other speech applications, in which case the
/cavs/hse/ies/www/data/mailing_lists/ies_staff/2005/archive/msg00008.html:They do all sorts of projects with speech recognition systems and other
/cavs/hse/ies/www/data/mailing_lists/ies_staff/2005/archive/msg00006.html:They do all sorts of projects with speech recognition systems and other
/cavs/hse/ies/www/data/mailing_lists/ies_staff/2005/archive/msg00003.html:They do all sorts of projects with speech recognition systems and other
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/1999/archive/msg00087.html: information - pitch periods, intonation, etc. and part of speech
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/1999/archive/msg00191.html:utterances concurrently using the same speech models, lexicon and
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/2000/archive/msg00119.html:> My code and makefile are in /home/u0/simpson/speech
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/2000/archive/msg00223.html:on programmers. I feel that most speech researchers, who understand
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/2000/archive/msg00287.html:of signal processing, and don't get too much speech specific stuff
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/2000/archive/msg00324.html:and looked at my speech code and this "+20" was initially in one of my
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/2000/archive/msg00481.html:the speech noise and also handle the text normalization. For this we
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/2000/archive/msg00482.html:> the speech noise and also handle the text normalization. For this we
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/2000/archive/msg01282.html:that is not explicitly a speech decoder and I think that, to date, we
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/2000/archive/msg01282.html:engine is again specific to speech and one particular type of speech
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/2000/archive/msg01299.html:> engine is again specific to speech and one particular type of speech
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/2001/archive/msg03137.html:Show me a plot of the speech signal and the energy signal.
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/2001/archive/msg03146.html:> Show me a plot of the speech signal and the energy signal.
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/2001/archive/msg03148.html:> > Show me a plot of the speech signal and the energy signal.
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/2001/archive/msg03149.html:> > > Show me a plot of the speech signal and the energy signal.
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/2001/archive/msg03150.html:> > > > Show me a plot of the speech signal and the energy signal.
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/2001/archive/msg03151.html:> > > > > Show me a plot of the speech signal and the energy signal.
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/2002/archive/msg00268.html:> > the speech class. Let's plan to meet and discuss some of
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/2002/archive/msg00312.html:the speech class. Let's plan to meet and discuss some of
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/2002/archive/msg00313.html:> the speech class. Let's plan to meet and discuss some of
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/2002/archive/msg00838.html:in the speech notes and other on-line material not convered in the
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/2002/archive/msg01586.html:Historically, NLP people preferred words on arcs and speech people
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/2002/archive/msg03477.html:As you know, an important design consideration in the production system is that we no introduce artificial constraints and requirements on the user. Remembering all the little tricks it takes to run a speech recognition system can easily cause cognitive overload (Julie can elaborate :)
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/2003/archive/msg00487.html: We use MFCC features as the input of the trainer and verifier. MFCC features are extracted from the training and testing speech. Periods of silence are removed from the speech prior to feature extraction using some kind of adaptive energy speech/silence detector.
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/2003/archive/msg00503.html:of frames of speech (of course, and silence) used for training a
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/2003/archive/msg00504.html:> of frames of speech (of course, and silence) used for training a
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/2003/archive/msg00504.html:The 3 second of utterance also includes the silence and so the speech
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/2004/archive/msg00165.html:UCSD. I am now working on speech recognition and encountered some problems
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/2004/archive/msg00166.html:> UCSD. I am now working on speech recognition and encountered
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/2004/archive/msg00380.html:object and the speech analysis can get the format info by using the
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/2004/archive/msg00391.html:object and gives it to the speech analysis. I think these two functins
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/2004/archive/msg00393.html:> object and gives it to the speech analysis. I think these two
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/2004/archive/msg00549.html: Same argument - speech exam and priority work for Thrusday Demos.
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/2004/archive/msg00567.html:and speech recognition bugs.
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/2004/archive/msg00568.html:> and speech recognition bugs.
/cavs/hse/ies/www/data/mailing_lists/ies_ifc/2004/archive/msg00568.html:The speech recognition "bugs" is very easy to fix. You take a file and
/cavs/hse/ies/www/data/mailing_lists/ies_support/1999/archive/msg00030.html:Netherlands, I am currently doing an article on speech recognition and
/cavs/hse/ies/www/data/mailing_lists/ies_support/1999/archive/msg00052.html:> Netherlands, I am currently doing an article on speech recognition and
/cavs/hse/ies/www/data/mailing_lists/ies_support/1999/archive/msg00075.html:In speech processing there is a renewed interest in saving memory and
/cavs/hse/ies/www/data/mailing_lists/ies_support/1999/archive/msg00140.html: I have downloaded your speech recognition software, and I have been
/cavs/hse/ies/www/data/mailing_lists/ies_support/1999/archive/msg00141.html:> I have downloaded your speech recognition software, and I have been
/cavs/hse/ies/www/data/mailing_lists/ies_support/1999/archive/msg00146.html:> > > I have downloaded your speech recognition software, and I have been
/cavs/hse/ies/www/data/mailing_lists/ies_support/1999/archive/msg00198.html:language continuous speech recognition and the system
/cavs/hse/ies/www/data/mailing_lists/ies_support/1999/archive/msg00217.html:and text to speech. we have also set up a lab for speech recognition and
/cavs/hse/ies/www/data/mailing_lists/ies_support/1999/archive/msg00218.html:and text to speech. we have also set up a lab for speech recognition and
/cavs/hse/ies/www/data/mailing_lists/ies_support/1999/archive/msg00260.html:I'm teaching assistant and speech recognition researcher at the Faculty for
/cavs/hse/ies/www/data/mailing_lists/ies_support/1999/archive/msg00369.html:Dear help@isip, I have become more interested in speech analysis, especially in the area for federal law enforcement. I used speech recognition software in the military to analyze sonar signals, determining pulse characteristics. They coorelate to each other in many areas. I am interested in stripping away background (ambient) noise to get the real signal of interest. Of the software programs offered, what do you recommend I start with in my research. I also have 0-very little budget and am using this as proof of concept for future funding. Thank you and have a good day.
/cavs/hse/ies/www/data/mailing_lists/ies_support/1999/archive/msg00381.html:> > > I have downloaded your speech recognition software, and I have been
/cavs/hse/ies/www/data/mailing_lists/ies_support/1999/archive/msg00395.html: I'm a student in the last course of telecommunicacion engineering. Actually I'm working on a project about speech recognition, and I´m using the ASR software. I`ve been trying to get the HTML pages of the tutorial you offer in tar format, but I haven´t been able. I would be grateful if you could send it to me.
/cavs/hse/ies/www/data/mailing_lists/ies_support/1999/archive/msg00398.html:> I'm a student in the last course of telecommunicacion engineering. Actually I'm working on a project about speech recognition, and I´m using the ASR software. I`ve been trying to get the HTML pages of the tutorial you offer in tar format, but I haven´t been able. I would be grateful if you could send it to me.
/cavs/hse/ies/www/data/mailing_lists/ies_support/1999/archive/msg00406.html:> > > > > I have downloaded your speech recognition software, and I have been
/cavs/hse/ies/www/data/mailing_lists/ies_support/1999/archive/msg00410.html:> > > > > > I have downloaded your speech recognition software, and I have been
/cavs/hse/ies/www/data/mailing_lists/ies_support/1999/archive/msg00411.html:> > > > > > > I have downloaded your speech recognition software, and I have been
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/maillist.html:
03/10/00: support: [ISIP #486] (help) about speech recognation and HMM:40,Bill Chapman via RT
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00023.html:> The sampling rate of the switchboard speech is 8000 Hz and the models available in
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00026.html:and playing raw data, and then, it was a clean speech signal.
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00056.html:> Have you tried your front-end on speech sampled at 16 kHz and compared to
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00057.html:> Have you tried your front-end on speech sampled at 16 kHz and compared to
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00092.html:> My name is Laurent Benarousse and I work in the speech recognition field
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00094.html:> bytes, it looks like speech data. I get the same sample_min and =
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00094.html:> sample_max in the header of speech file.( I just fseek 1024 byte and =
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00105.html:in speech research and education.
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00111.html:you would use netscape to access the speech system. The complete tutorial and
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00119.html:Look at the publications and courses related to speech that we have online, if
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00121.html:> I'm working on a project about speech recognition and I'm using the
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00127.html:
support: [ISIP #486] (help) about speech recognation and HMM
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00127.html:
support: [ISIP #486] (help) about speech recognation and HMM
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00128.html:
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00128.html:
support: [ISIP #486] (help) about speech recognation and HMM
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00128.html:
support: [ISIP #486] (help) about speech recognation and HMM
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00128.html:> some commands and operate some devices.There for I research HMM for speech
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00128.html:> recognation and how it is used for speech recognation.If you send some
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00129.html:
support: [ISIP #486] (help) about speech recognation and HMM
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00129.html:
support: [ISIP #486] (help) about speech recognation and HMM
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00130.html:> now I am working on a project related to speech recognition and would be
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00137.html:> i am working in project in speech recognition and a have some bugs in my
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00139.html:fundermental works on it and then I will be able to do research in speech
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00152.html:and also to release a new version of the speech recognition package, by Friday
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00200.html:> > Have you tried your front-end on speech sampled at 16 kHz and compared to
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00225.html:B20 release, and make version 3.75 to compile and run our prototype speech
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00227.html:> speech and gaze for HCI.
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00243.html:> My name is Laurent Benarousse and I work in the speech field in France.
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00287.html:> My name is Christina Kyriakou and I work on a speech recognition project
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00313.html:you may need a speech recognition system to detect and distinguish spoken
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00319.html:> glass to collect the speech signals arround and using
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00319.html:> time and have developed the software of speech recognition:
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00331.html:(in files), and then the speech recognition system works with these files,
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00355.html:web pages for the tutorial and sample files for running the speech recognition
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00373.html:I sent your problem to one of our speech experts, and here is her answer:
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00373.html:> I am working on a speech recognition project, using isip_proto v5.6, and I
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/archive/msg00411.html:> I have speech data for which I know for each phone when it starts and
/cavs/hse/ies/www/data/mailing_lists/ies_support/2000/index.html:
support: [ISIP #486] (help) about speech recognation and HMM,
/cavs/hse/ies/www/data/mailing_lists/ies_support/2001/archive/msg00010.html:For example, try "automatic speech recognition" and look at the 3rd match.
/cavs/hse/ies/www/data/mailing_lists/ies_support/2001/archive/msg00036.html:for large vocabulary speech recognition) and extend them after each
/cavs/hse/ies/www/data/mailing_lists/ies_support/2001/archive/msg00052.html:I have a small question. It's a bit premature because I have not explored your speech recognizer well enough, and also I'm a novice to the field. When you train on connected digits using TIDIGITS, it appears to me from the package that the labels you use do not have any time information, that is the labels do not specify where each digit in a sequence of digits lie on the time scale. Is that information not required by your system, or you generate it while training (that is, you find boundaries)? Please let me know. Thanks a lot.
/cavs/hse/ies/www/data/mailing_lists/ies_support/2001/archive/msg00053.html:> explored your speech recognizer well enough, and also I'm a novice
/cavs/hse/ies/www/data/mailing_lists/ies_support/2001/archive/msg00206.html:I'm very interested in speech recognition. Where can I find some basic and
/cavs/hse/ies/www/data/mailing_lists/ies_support/2001/archive/msg00207.html:> I'm very interested in speech recognition. Where can I find some basic and
/cavs/hse/ies/www/data/mailing_lists/ies_support/2001/archive/msg00269.html:I am doing some hobby dabbling in the area of robotics and speech
/cavs/hse/ies/www/data/mailing_lists/ies_support/2001/archive/msg00273.html:TIDIGITS toolkit and thought that this would help me building a speech
/cavs/hse/ies/www/data/mailing_lists/ies_support/2001/archive/msg00274.html:TIDIGITS toolkit and thought that this would help me building a speech
/cavs/hse/ies/www/data/mailing_lists/ies_support/2002/archive/msg00029.html:I'm a French young computer science engineer and I'm working on a speech
/cavs/hse/ies/www/data/mailing_lists/ies_support/2002/archive/msg00030.html:> I'm a French young computer science engineer and I'm working on a speech
/cavs/hse/ies/www/data/mailing_lists/ies_support/2002/archive/msg00031.html:The programme is supposed to be having the sampling points of speech sample and detecting the dominant pitch frequency of that person.
/cavs/hse/ies/www/data/mailing_lists/ies_support/2002/archive/msg00038.html:clients for the speech recognition and returns answers back. My teacher =
/cavs/hse/ies/www/data/mailing_lists/ies_support/2002/archive/msg00041.html:I'm a undergrad student wishing to do a project involving speech recognition . I came across your ISIP Foundation classes and found them interesting . I have downloaded the entire thing . I found out that the dictionary consists of only monophones and tht too only words like one to ten . I want to build a speech recognition engine with my own set of dictionary and i want it to be in the triphone model .
/cavs/hse/ies/www/data/mailing_lists/ies_support/2002/archive/msg00179.html:I would like to ask you about the different types speech recognition(SR) systems, about different techniques that are used for SR, their strengths and weaknesses and which is the best among them. It would be very kind of you if you could send me materials or links to materials that deal with the above-mentioned things in detail.
/cavs/hse/ies/www/data/mailing_lists/ies_support/2002/archive/msg00179.html:I would also like to ask you about the different components that make up a SR system and whether a speech engine already incorporates a vocabulary.
/cavs/hse/ies/www/data/mailing_lists/ies_support/2002/archive/msg00180.html:I would like to ask you about the different types speech recognition(SR) systems, about different techniques that are used for SR, their strengths and weaknesses and which is the best among them. It would be very kind of you if you could send me materials or links to materials that deal with the above-mentioned things in detail.
/cavs/hse/ies/www/data/mailing_lists/ies_support/2002/archive/msg00180.html:I would also like to ask you about the different components that make up a SR system and whether a speech engine already incorporates a vocabulary.
/cavs/hse/ies/www/data/mailing_lists/ies_support/2002/archive/msg00186.html:My name is Kofi and I am a student at the University of Cape Town in South Africa. I am currently attempting a thesis project on accent identification. i am using the speech samples in the TIMIT database. the quest is to take a speech sample and to determine the speakers dialect region of origin. in short, take the speech samples, extract features vectors and build 8 models representing the eight dialect regions. i tried to download the Tidigit toolkit in the hope that i might use it in my work but the link on your website is not working.
/cavs/hse/ies/www/data/mailing_lists/ies_support/2002/archive/msg00197.html:and build simple speech recognizers for test purposes and for demo
/cavs/hse/ies/www/data/mailing_lists/ies_support/2002/archive/msg00225.html:This has been used for many years by several speech research groups. It has never been published. You might find code on the NIST speech group web site, and perhaps a publication or two on it.
/cavs/hse/ies/www/data/mailing_lists/ies_support/2002/archive/msg00251.html:My name is Boris and I would like to ask you for some information about vector quantization, because my project on my University consist of vector quantization applied on speech recognition. I will do it through genetic algorithms.
/cavs/hse/ies/www/data/mailing_lists/ies_support/2003/archive/msg00010.html:between speech and laughter? Or do you want to use an
/cavs/hse/ies/www/data/mailing_lists/ies_support/2003/archive/msg00028.html:> speech recognition system to download and use it,
/cavs/hse/ies/www/data/mailing_lists/ies_support/2003/archive/msg00032.html:on "clean" speech and evaluate on data underrepresented in the
/cavs/hse/ies/www/data/mailing_lists/ies_support/2003/archive/msg00035.html:task is to be done in Java and right now we are very much in need of a speech
/cavs/hse/ies/www/data/mailing_lists/ies_support/2003/archive/msg00104.html:Specially I want to implement my own speech and I want to get any report, but ISIP doesn't work at all.
/cavs/hse/ies/www/data/mailing_lists/ies_support/2003/archive/msg00195.html:> speech as input and give the phonemes as output.
/cavs/hse/ies/www/data/mailing_lists/ies_support/2003/archive/msg00262.html:> I'm a PhD Student at LORIA in France, and I work on speech
/cavs/hse/ies/www/data/mailing_lists/ies_support/2003/archive/msg00275.html:My name is Norelli Schettini and I'm working in a speech recognition project at my University (Universidad del Norte-Colombia).
/cavs/hse/ies/www/data/mailing_lists/ies_support/2004/archive/msg00017.html:I'm speech researcher and also user of several speech recognition
/cavs/hse/ies/www/data/mailing_lists/ies_support/2004/archive/msg00023.html:> > the advantage of this, and should I use this technique in the speech
/cavs/hse/ies/www/data/mailing_lists/ies_support/2004/archive/msg00053.html: I am a French student who work on a speech recognition project and I
/cavs/hse/ies/www/data/mailing_lists/ies_support/2004/archive/msg00054.html:> I am a French student who work on a speech recognition project and I
/cavs/hse/ies/www/data/mailing_lists/ies_support/2004/archive/msg00065.html:the speech to common measurements like color sustem and any mix between
/cavs/hse/ies/www/data/mailing_lists/ies_support/2005/archive/msg00003.html:I have read your tutorial about the speech recognition. i want to get more details about the phone model training. this should explain how we initialize it, training it from transcription (sentences) and the algorithms.
/cavs/hse/ies/www/data/mailing_lists/ies_support/2005/archive/msg00010.html:> > in the areas of speech recognition and wireless communications
/cavs/hse/ies/www/data/mailing_lists/ies_support/2005/archive/msg00017.html:turnkey hardware, software and world-class text-to-speech and speech
/cavs/hse/ies/www/data/mailing_lists/ies_support/2005/archive/msg00018.html:interesting in speech recognition (voice command
/cavs/hse/ies/www/data/mailing_lists/ies_dialog/2005/archive/msg00021.html:speakers. I remember Tang and Gao collected our speech data in end of
/cavs/hse/ies/www/mission/isip/mission_v1.html:and the state of speech research are striking. Einstein entered the field
/cavs/hse/ies/www/projects/dialog/OLD/index2.html:mycontent[0]='Learn about basic digital voice speech processing by recording and visualizing your voice patterns.'
/cavs/hse/ies/www/projects/southern_accents/index.html:Dragon Systems, Inc., a world leader in speech recognition technology conducted a data collection in Mississippi State University starting from February 21 till February 25. The goal of this project was to collect southern accented data. Speech collection is one of the essential and efficient way to develop proper interface between the people and the machines. The recognizers that have been developed so far failed badly in recognizing voices of speakers with southern accents. This was the cause of lack of sufficient speakers with southern accents in the database. So in order to train the recognizer to perform effectively even with speakers with southern accent this speech collection was conducted. Speaker were selected according to the following criteria
/cavs/hse/ies/www/projects/speech/software/downloads/html/archives_2001.html: Recommended for serious speech and signal processing
/cavs/hse/ies/www/projects/speech/software/downloads/html/archives_2001.html: speech technologists and application developers.
/cavs/hse/ies/www/projects/speech/software/downloads/html/archives_2002.html: LVCSR system. Recommended for speech technologists and
/cavs/hse/ies/www/projects/speech/software/downloads/html/archives_2002.html: speech technologists and application developers. In this
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/aaas00/html/program.html:speech recognition, speaker recognition, and speech synthesis make
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/aaas00/html/program.html:was said), speaker recognition (who said it) and speech synthesis
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/aaas00/html/program.html:and as accessible as online text? In the field, speech recognition is
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/aaas00/html/program.html:of speech recognition ubiquitously used as both information access and
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/aaas00/html/program.html:increasingly important for natural, effective and secure speech
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/aaas00/html/program.html:state-of-the-art speech synthesis, and project the future of natural
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/aaas00/html/title.html:speech recognition, speaker recognition, and speech synthesis make
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/asru97/main/index.html:new research directions in speech recognition and understanding
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/asru97/main/index.html:technologies, and applications of speech understanding systems.
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/asru97/menu/call_for_papers/index.html:automatic speech recognition and understanding, with robust modeling
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/asru97/menu/other_conferences/index.html:Below are other conferences and workshops related to speech processing:
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/asru97/menu/workshop_program/abstracts/abstract_11.html:given speech instant and suggests temporal dynamics of components
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/asru97/menu/workshop_program/abstracts/abstract_18.html:This paper reports on topic extraction in Japanese broadcast-news speech. We studied, using continuous speech recognition, the extraction of several topic-words from broadcast-news. A combination of multiple topic-words represents the content of the news. This is more detailed and more flexible than a single word or a single category. A topic-extraction model shows the degree of relevance between each topic-word and each word in the articles. For all words in an article, topic-words which have high total relevance score are extracted from the article. We trained the topic-extraction model with five years of newspapers, using the frequency of topic-words taken from headlines and words in articles. The degree of relevance between topic-words and words in articles is calculated on the basis of statistical measures, i.e., mutual information or the chi-sequare-value. In topic-extraction experiments for recognized broadcast-news speech, we extracted five topic-words using a chi-square-based model and found that 75% of them agreed with topic-words chosen by subjects.
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/asru97/menu/workshop_program/abstracts/abstract_22.html:We propose using Hidden Markov Models (HMMs) associated with the cepstrum coefficients to remove disturbances that degrade the speech recognition process. In order to perform this task in an on-line manner, we use the MUltipath Stochastic Equalization (MUSE) framework. This method allows one to process data at the frame level. Two equalization functions are examined in this paper; bias removal and linear regression. Recognition experiments carried out on both PSTN and GSM networks show the efficiency of the proposed method: thanks to MUSE, with a model trained on PSTN recorded digits, the error rate on both PSTN and GSM recorded digits can be reduced by 19% with bias subtraction and by 36% with linear regression. Similar results obtained on another vocabulary are also presented.
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/asru97/menu/workshop_program/abstracts/abstract_24.html:The use of speech recognition systems shows that noise and channel effects are very disturbing, and an efficient detection of speech/non-speech segments is necessary. Preprocessing the speech signal is one of the adopted solutions to improve recognition performance. In this paper, spectral subtraction is used as a preprocessing technique aiming to increase the robustness to noisy conditions. Results of several experiments carried out on a database collected over GSM network show that spectral subtraction improves the global recognizer performances, especially in very noisy environments. We show that the improvements concern mainly noise/speech detection module.
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/asru97/menu/workshop_program/abstracts/abstract_34.html:noise and resulting noisy speech is non-linear. Thus, existing
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/asru97/menu/workshop_program/abstracts/abstract_34.html:of clean speech and noise. Assuming that the difference between these
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/asru97/menu/workshop_program/abstracts/abstract_39.html:and effective for the speech recognition system.
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/asru97/menu/workshop_program/abstracts/abstract_4.html:begun building a universal speech recognizerfor English and
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/asru97/menu/workshop_program/abstracts/abstract_4.html:French and English and describe speech recognition results for the
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/asru97/menu/workshop_program/abstracts/abstract_41.html:We propose a new technique that compensates for an acoustic mismatch. This technique is simple and can estimate the acoustic mismatch more accurately than conventional Cepstrum Mean Normalization (CMN), because it takes into considera-tion the kind of phonemes and their frequency, and can calculate the acoustic mismatch in detail. In this procedure the acoustic mismatch can be estimated as the difference between the centroid vector of distorted speech and that of acoustic models. The cepstral mean of distorted speech is the centroid vector including the distortion. The centroid vector calculated from parameters of acoustic models is regarded as the centroid vector when the distorted speech is assumed to be clean speech. The acoustic models used for calculation are for phonemes that appear in the transcription of the speech. This technique achieves a high word error reduction rate of 73% for ordinary analog telephone speech and 70% for wireless telephone handset speech.
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/asru97/menu/workshop_program/abstracts/abstract_45.html:and speech recognition. The paper consists of two parts. The first
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/asru97/menu/workshop_program/abstracts/abstract_48.html:modeling of speech units using word models for letters and phone
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/asru97/menu/workshop_program/abstracts/abstract_68.html:classification and speech recognition into one algorithm. This is accomplished
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/asru97/menu/workshop_program/abstracts/abstract_69.html:We present constrained time alignment acoustic models based on phonetic knowledge and a speaker independent speech recognition method with the models. Japanese syllable and isolated word recognition experiments show that the models have
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/asru97/menu/workshop_program/abstracts/abstract_73.html:mismatch between training and testing in speech recognition applications and
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/asru97/menu/workshop_program/abstracts/abstract_76.html:trained and used to locate speech events that bear distinctive signatures
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/asru97/menu/workshop_program/plenary_talks/ckamm.html:and robust speech recognition and language modeling, but also iterative,
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/asru97/menu/workshop_program/plenary_talks/jflanagan.html:Utilizing speech in combination with simultaneous visual gesture and haptic
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/asru97/menu/workshop_program/plenary_talks/ldeng.html:and the continuous, dynamic phonetic processes in human speech production
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/asru97/menu/workshop_program/plenary_talks/ldeng.html:and is developed to aim at overcoming key limitations of current speech
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/asru97/menu/workshop_program/plenary_talks/lpols.html:in speech understanding, and in dialogue handling, we generally test human
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/asru97/menu/workshop_program/plenary_talks/lpols.html:nature. In speech and language technology we would like to equal, or
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/asru97/menu/workshop_program/plenary_talks/lpols.html:lessons to be learned for designing speech and language technology systems.
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/asru97/menu/workshop_program/plenary_talks/mrussel.html:and robust speech recognition and language modeling, but also iterative,
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/asru97/menu/workshop_program/plenary_talks/rlippmann.html:and with interruptions of the speech input. A new and simple approach
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srsdr00/html/title.html:But, where else can you learn about speech recognition, and enjoy
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srsdr00/technical_program/session_01/html/resources.html: We have several speech and signal processing courses on-line,
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srsdr00/technical_program/session_02/history/html/hist_02.html: speech databases and software.
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srsdr00/technical_program/session_02/history/html/hist_02.html: speech databases, software, and performance analysis.
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srsdr01/misc/feedback/feedback.html:Workshop was very informative. I was really impressed with how well your students conducted each session of the workshop. Now, relating to the SRSD, as a novice in this area, I felt well informed and gained an enormous amount of knowledge about the system. I think that your system is a wonderful tool and I will recommend it to the gentleman student who is working on speech recognition at NCA&T. In addition, I would like to thank you all for your professionalism and hospitality while I was at your prestigious university and program of Electrical and Computer Engineering.
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srsdr01/program/session_02/benchmarks/html/gui_01.html:
This page contains a collection of research papers, journal publications and dissertations / theses that we find as useful reference materials for speech and digital signal processing research.
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srsdr02/misc/feedback/feedback.html:The ease-of-use aspect of the research of ISIP is very refreshing. If you wanted to really evangelize your system, you could start spreading the word at other universities, not necessarily only the speech expert ones, forming collaborations, and showing shared results.
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srsdr02/misc/misc/feedback/feedback.html:Workshop was very informative. I was really impressed with how well your students conducted each session of the workshop. Now, relating to the SRSD, as a novice in this area, I felt well informed and gained an enormous amount of knowledge about the system. I think that your system is a wonderful tool and I will recommend it to the gentleman student who is working on speech recognition at NCA&T. In addition, I would like to thank you all for your professionalism and hospitality while I was at your prestigious university and program of Electrical and Computer Engineering.
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srsdr02/program/session_01/benchmarks/html/gui_01.html: This page contains a collection of research papers, journal publications and dissertations / theses that we find as useful reference materials for speech and digital signal processing research.
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srsdr02/program/session_01/benchmarks/html/spine_02_00.html:and Helo). The coded speech data sets are generated from the above
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srsdr02/program/session_01/benchmarks/html/spine_02_00.html: Used all the uncoded and coded speech data provided in the
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srsdr02/program/session_01/benchmarks/html/spine_02_02.html: and vocoded speech
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srsdr02/program/session_04/demonstrations/html/arch_02.html:Usability: A client provides friendly interface support, which can be a GUI or voice interaction; a server does most of the computation like signal processing and decoding in speech system.
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srstw00/html/title.html:Summer at Mississippi State means two things: baseball and a speech
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srstw00/misc/feedback/feedback.html:I would like to start the comments session by thanking you all at ISIP for the wonderful time I had at SRSTW00.As a novice in the field of speech recognition and with barely any background in the same area, I am happy to mention that I have picked up quite a lot of information in the field of SPEECH RECOGNITION. Well I do have certain suggestions to make, which I think shall be handy for the forthcoming workshops in MSU. The area of major concern is the LAB sessions. I felt a lot was cramped into the lab sessions. It would be more helpful if the LABS were focused on one issue at a time.
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srstw00/misc/feedback/feedback.html:For a beginner in the ASR field the SRSTW workshop was immensely helpful. I got a very good introduction to the basic techniques, especially acoustic modelling, although I would have liked to see some more material on Language modelling. Coming from a DSP background, with little C++ programming experience, I now have some idea about the difficulties involved in the software engineering process dealing with a project of such a grand scope. I am now very motivated about using the ISIP system and learning as much as possible about speech recognition, although the computing resources available to me are very limited. Finally, the lectures by Dr. Picone and Arvind were really helpful. The labs were a little dissapointing, although that can be expected since this is the first workshop. I would like to see the labs to be actually worked out in detatil before the participants were to do them. The care and hospitality shown by the ISIP group was commendable, and I would especially like to thank Bill Chapman for his efforts. Overall I think the workshop was a great success and I would like to come back next year.
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srstw00/misc/feedback/feedback.html:of material within speech recognition and
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srstw00/misc/feedback/feedback.html:We experienced some great “Southern Hospitability”. They made sure that all the participants were comfortable. However, the whole idea of the workshop was to teach about the ISIP system. But for a novice...it would have been have much more comprehendible if there was “at least” one session describing the basics of speech recognition and the terms used in latter sessions during the workshop (or it should have at least been documented).
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srstw00/misc/feedback/feedback.html:I consider the workshop a success, and my attendance beneficial to my own knowledge of speech recognition. My research will tend to lead to the use of speech recognition at the application level, so my interest was more in what goes on "underneath the hood". The lectures as well as labs definitely gave a good overall look at what speech recognition systems are made of. It was also interesting in seeing how the ISIP system worked, and why they have chosen certain paths in their implementation.
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srstw00/program/session_02/introduction/html/feedback.html:We experienced some great “Southern Hospitability”. They made sure that all the participants were comfortable. However, the whole idea of the workshop was to teach about the ISIP system. But for a novice...it would have been have much more comprehendible if there was “at least” one session describing the basics of speech recognition and the terms used in latter sessions during the workshop (or it should have at least been documented).
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srstw00/program/session_02/introduction/html/feedback.html:I consider the workshop a success, and my attendance beneficial to my own knowledge of speech recognition. My research will tend to lead to the use of speech recognition at the application level, so my interest was more in what goes on "underneath the hood". The lectures as well as labs definitely gave a good overall look at what speech recognition systems are made of. It was also interesting in seeing how the ISIP system worked, and why they have chosen certain paths in their implementation.
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srstw00/program/session_11/systems/html/exercise_00.html: Download the SWITCHBOARD CONVERSATION and extract features from the speech file:
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srstw00/program/session_11/systems/html/exercise_00.html: Use the scripts provided in the package to segment the speech file and generate mfcc's.
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srstw01/misc/feedback/feedback.html:WHOA! MSU-ISIP-SRSTW *HAS* to be the best-kept secret in speech rec! WHAT a SERVICE to the INDUSTRY! My compliments on an INCREDIBLE team – knowledge, insight, work ethic, can-do attitude and warm hospitality – a combination seldom found in industry, let alone in an academic research group! Your group is the envy of most academic research professors.
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srstw01/misc/feedback/feedback.html: - RESOURCE LIST up front – maybe a list of who the primary players are in speech research outside of ISIP – including related areas in ADDITION to speech rec – like TTS [Text To Speech] and AVR [Automated Voice Recognition] – maybe also a list of recommended conferences and workshops outside of ISIP for those who wish to more carefully follow the state of the art in the future.
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srstw01/misc/feedback/feedback.html:The workshop was very well organised. The speech recognition system has a lot of possibilities,and the lectures and lab sessions gave a good overview of what can be achieved with it.
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srstw01/misc/feedback/feedback.html:SRSTW'01 was interesting, informative and useful. It successfully catered for a very wide range of abilities, affording the flexibility to take people new to speech recognition, like myself, through the processes step by step, but at the same time allowed the experts room to explore the finer points if they wanted. That's some achievement. The morning sessions were nice and relaxed. A couple of minor criticisms; the less experienced speakers could pace their talks more slowly. It takes time for an audience to absorb what you have just said, even if it seems slow when you're up there at the front. Secondly, this is acronym city. A glossary/list of terms somewhere at the back of the notes would have been useful. Also, despite the sea of equations, you don't have to be a genius at maths to understand enough of what's going on here. Being told that at the outset would have been reassuring. The labs were well organized, though having everyone install at the same time is obviously not a good idea. You don't need great expertise in C++ or unix, but perhaps some clarification as to how and where they will be used would be useful for future attendees. Presented with a bunch of example templates in the morning, I thought the coding in the labs might be quite intensive, and of course this isn't the case. Helpful and un-judgmental lab support was a real asset. Of course, more nice GUIs would be good too…
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srstw01/misc/feedback/feedback.html:Finally, a big thank you to everyone who helped out on this course. Your hospitality was exemplary, warm and friendly, trips out most evenings, etc. OK maybe there isn't any food you can buy on campus after 5pm, but the cookies, Danish pastries, fruit, bagels, and veggie dips that just appear during the day take the edge off that. There were enough cars to go round if you don't have one, but perhaps you could suggest people bring a few supplies for the fridge if they're staying on campus and don't have wheels. At least warn them that the centRE of Starkville is too far to walk to. All in all, I've learnt a lot about speech recognition - and I've had fun too. The colonials done good. Judy Tryggvason.
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srstw01/program/session_02/introduction/html/feedback.html:We experienced some great “Southern Hospitability”. They made sure that all the participants were comfortable. However, the whole idea of the workshop was to teach about the ISIP system. But for a novice...it would have been have much more comprehendible if there was “at least” one session describing the basics of speech recognition and the terms used in latter sessions during the workshop (or it should have at least been documented).
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srstw01/program/session_02/introduction/html/feedback.html:I consider the workshop a success, and my attendance beneficial to my own knowledge of speech recognition. My research will tend to lead to the use of speech recognition at the application level, so my interest was more in what goes on "underneath the hood". The lectures as well as labs definitely gave a good overall look at what speech recognition systems are made of. It was also interesting in seeing how the ISIP system worked, and why they have chosen certain paths in their implementation.
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srstw01/program/session_11/switchboard/html/title.html: Generate and rescore word graphs to recognize conversational speech data
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srstw02/misc/feedback/feedback.html:I think that having the first lab on IFC has the disadvantage that at that point we don't know much about the system and speech recognition. So, the examples must be somehow trivial. Maybe a session about programming with IFC could be postponed to a later day and we could eventually go over some more elaborated tasks like changing a step in the front-end (or choosing among a few of different tasks).
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srstw02/misc/feedback/feedback.html:Overall I found the workshop excellent. Prof. Picone's lectures are great, all students are very friendly and willing to share their knowledge. Talking with us during lunch hour is a nice idea. I learned a lot and hope to contribute with the ISIP software in future. In fact, in this speech recognition arena, full of "dark sites", the force is strong with ISIP !
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srstw02/program/session_02/introduction/html/feedback.html:We experienced some great “Southern Hospitability”. They made sure that all the participants were comfortable. However, the whole idea of the workshop was to teach about the ISIP system. But for a novice...it would have been have much more comprehendible if there was “at least” one session describing the basics of speech recognition and the terms used in latter sessions during the workshop (or it should have at least been documented).
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srstw02/program/session_02/introduction/html/feedback.html:I consider the workshop a success, and my attendance beneficial to my own knowledge of speech recognition. My research will tend to lead to the use of speech recognition at the application level, so my interest was more in what goes on "underneath the hood". The lectures as well as labs definitely gave a good overall look at what speech recognition systems are made of. It was also interesting in seeing how the ISIP system worked, and why they have chosen certain paths in their implementation.
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srstw02/program/session_05/transforms/html/concepts.html:An isip speech transformation utility, which transforms data and
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srstw02/program/session_05/transforms/html/concepts.html:FrontEnd processes input speech signal data and generates
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srstw02/program/session_05/transforms/html/intro_00.html:- FrontEnd processes speech signal data and uses the specified
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srstw02/program/session_07/model_design/html/title.html:
- Learn how we initialize and design models for a continuous speech recognition system.
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srstw03/program/session_01/introduction/html/intro_01.html: public domain speech recognition (software and toolkits).
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srstw03/program/session_01/scoring/html/scoring_00.html:
- Words are the smallest units of speech sounds that symbolizes and communicates a meaning without being divisible into smaller units.
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srstw03a/program/session_01/feature/html/feature_00.html:
- FrontEnd processes speech signal data and uses the specified
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srstw03a/program/session_01/introduction/html/intro_01.html: public domain speech recognition (software and toolkits).
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srstw03a/program/session_01/scoring/html/scoring_00.html:
- Words are the smallest units of speech sounds that symbolizes and communicates a meaning without being divisible into smaller units.
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srstw03a/program/session_01/software/html/software_00a.html: System. Recommended for speech technologists and application
/cavs/hse/ies/www/projects/speech/software/tutorials/conferences/srstw03a/program/session_01/software/html/software_00a.html: trainer. Recommended for serious speech and signal
/cavs/hse/ies/www/projects/speech/software/tutorials/monthly/2001/2001_09_workshops/index.html:train and evaluate a large vocabulary speech recognition system.
/cavs/hse/ies/www/projects/speech/software/tutorials/monthly/2001/2001_09_workshops/index.html:tutorials and lecture notes on speech recognition.
/cavs/hse/ies/www/projects/speech/software/tutorials/monthly/2002/2002_07_tutorial_book/index.html: features of a person's speech and store the measurements in feature
/cavs/hse/ies/www/projects/speech/software/tutorials/monthly/2002/2002_08_software_engineering/index.html:the parameters of the acoustic models given the speech data and
/cavs/hse/ies/www/projects/speech/software/tutorials/monthly/2003/2003_02_web/index.html:of our program is to educate students and researchers who are new to speech
/cavs/hse/ies/www/projects/speech/software/tutorials/monthly/2003/2003_02_web/index.html:the field. Our speech site is a dedicated resource for any and all interested
/cavs/hse/ies/www/projects/speech/software/tutorials/monthly/2003/2003_02_web/index.html:decoder and a network trainer recommended for serious speech and signal
/cavs/hse/ies/www/projects/speech/software/tutorials/monthly/2003/2003_02_web/index.html:and even create custom signals. From any page within the speech site, click
/cavs/hse/ies/www/projects/speech/software/tutorials/monthly/2003/2003_02_web/index.html:production and perception. Included are demos to illustrate speech
/cavs/hse/ies/www/projects/speech/software/tutorials/monthly/2003/2003_09_tang/index.html:distributed and embedded speech computing. I proposed an innovative
/cavs/hse/ies/www/projects/speech/software/tutorials/monthly/2003/2003_09_tang/index.html:naturalness speech synthesis systems on resource-sensitive mobile and
/cavs/hse/ies/www/projects/speech/software/tutorials/monthly/2003/2003_09_tang/index.html:to speech recognition. I am sure the experience and knowledge I gain
/cavs/hse/ies/www/projects/speech/software/tutorials/monthly/2003/2003_09_tang/index.html:complicated and challenging speech recognition problems.
/cavs/hse/ies/www/projects/speech/software/tutorials/monthly/2003/2003_10_dialog/index.html:request which is received by the speech recognition module and parsed by the NLU module. If there is
/cavs/hse/ies/www/projects/speech/software/tutorials/monthly/2003/2003_11_verification/index.html:Speaker verification is the use of machine to verify a person's claimed identity from his voice. As a moderate-cost, unforgettable and unobtrusive biometric, human speech has been intensely demanded to use to identify a customer in many applications such as information or services access control and telephone banking. Therefore, speaker verification technology has brought about many research and engineering efforts both in the academia and industry.
/cavs/hse/ies/www/projects/speech/software/tutorials/monthly/2003/2003_11_verification/index.html:State University, I am pleased to announce the first release of the ISIP speaker verification system, which was developed using our speech recognition system known as the production system. The ISIP speaker verification system takes MFCC acoustic features as input and outputs acceptance/rejection hypotheses. The following figure represents the architecture of this process.
/cavs/hse/ies/www/projects/speech/software/tutorials/monthly/2004/2004_04/index.html: speech data of a specific speaker, and output is
/cavs/hse/ies/www/projects/speech/software/tutorials/monthly/2004/2004_06/index.html: and would match the phrase "a simple speech sequence", with no
/cavs/hse/ies/www/projects/speech/software/tutorials/monthly/2004/2004_06/index.html: and would match "a very simple speech sequence", "a typical speech
/cavs/hse/ies/www/projects/speech/software/tutorials/monthly/2004/2004_06/index.html: sequence", and "a very complex speech sequence".
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/glossary/index.html: attribute of speech needed by the recognizer to differentiate words and
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/glossary/index.html: creates and configures the speech input format, the algorithms for
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/glossary/index.html: creates and configures the speech input format, the algorithms for
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/glossary/index.html: measurements and conveys a smoother representation of the speech data.
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/section_02/s02_01_p02.html: The content and format of a speech file header varies
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/section_02/s02_04_p02.html: is preparation and coordination of speech audio data and speech
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/section_02/s02_04_p02.html: storing and accessing speech data and transcriptions through two related
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/section_02/s02_04_p02.html: Storage and access to speech data files is managed through an
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/section_02/s02_04_p02.html: The start and stop times are optional, and denote where the speech
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/section_03/images/ARCHIVE/s02/oldfiles/section_02_01_02.html:and format of a speech file header varies
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/section_03/images/ARCHIVE/s02/oldfiles/section_02_02_03.html:Listening to the converted speech data and comparing file size are two
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/section_03/images/ARCHIVE/s02/oldfiles/section_02_04_02.html:ISIP provides a method for storing and accessing speech
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/section_03/images/ARCHIVE/s02/oldfiles/section_02_04_02.html:Storage and access to speech data files is managed through an
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/section_03/images/ARCHIVE/s02/oldfiles/section_02_04_02.html:and 3) the starting and ending time of the speech signal stored in the file.
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/section_03/s03_01_p03.html: input the speech to the feature extraction algorithms and output the
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/section_03/s03_04_p01.html: used by the speech recognizer. The command line and argument
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/section_04/old/index.html: automatic speech recognition. Once the acoustic and language
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/section_04/old/s04_01_p02.html: Continuous speech recognition is both a pattern recognition and search
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/section_04/old/s04_01_p03.html: located and time-aligned with the speech data. An example of this is shown
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/section_04/index.html: automatic speech recognition. Once the acoustic and language
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/section_04/s04_01_p02.html: Continuous speech recognition is both a pattern recognition and search
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/section_04/s04_01_p03.html: located and time-aligned with the speech data. An example of this is shown
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/section_05/ARCHIVE/s05_01_p01.html: by a speech recognizer for decoding reflect this definition and are
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/section_05/ARCHIVE/s05_01_p03.html:observe a speech researcher in these three states over many days and
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/section_05/ARCHIVE/s05_01_p04.html: To initialize and train such a model for speech recognition,
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/section_05/ARCHIVE/s05_02_p03.html:lexicon, language model, acoustic model, speech data, and the transcriptions.
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/section_05/ARCHIVE/s05_02_p03.html:details how to produce the transcription and speech databases.
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/section_05/s05_01_p01.html: by a speech recognizer for decoding reflect this definition and are
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/section_05/s05_01_p03.html:observe a speech researcher in these three states over many days and
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/section_05/s05_01_p04.html: To initialize and train such a model for speech recognition,
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/section_06/ARCHIVE/s05_03_p02.html:(Carnegie Mellon University) toolkit, to decode a speech signal and
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/section_06/ARCHIVE/s06_01_p01.html:for speech recognition, N-grams and
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/section_06/old/index.html: features from speech and how to use them to build acoustic models.
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/section_06/old/s06_01_p01.html:for speech recognition, N-grams and
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/section_06/old/s06_02_p06.html:for building and applying statistical language models for speech recognition.
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/section_06/index.html: features from speech and how to use them to build acoustic models.
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/section_06/s06_01_p01.html:for speech recognition, N-grams and
/cavs/hse/ies/www/projects/speech/software/tutorials/production/fundamentals/v1.0/section_06/s06_02_p06.html:for building and applying statistical language models for speech recognition.
/cavs/hse/ies/www/projects/speech/software/tutorials/prototype/alphadigits/v1.0/data_prep/data_prep.html: Word and model label files for each speech segment are required
/cavs/hse/ies/www/projects/speech/software/tutorials/prototype/alphadigits/v1.0/index.html: evaluation scoring and for speech file manipulation. These can
/cavs/hse/ies/www/projects/t1_interface/html/swb.html:speech and text are essential to progress in speech and speaker
/cavs/hse/ies/www/projects/voice_analysis/downloads/releases/temp/isip_r00_n11_t03/class/asr/SpeechRecognizer/index.html:
- This example shows how to set up and run the speech recognizer
/cavs/hse/ies/www/projects/voice_analysis/downloads/releases/temp/isip_r00_n11_t03/class/algo/FourierTransform/index.html:The Discrete Cosine Transform (DCT) is widely used for speech and
/cavs/hse/ies/www/projects/voice_analysis/downloads/releases/temp/isip_r00_n11_t03/class/mmedia/index.html: and speech language model formats in this library.
/cavs/hse/ies/www/projects/voice_analysis/downloads/releases/temp/isip_r00_n11_t03/class/mmedia/JSGFParser/index.html:and decoding in the ISIP speech recognizer. For details on JSGF, see:
/cavs/hse/ies/www/projects/voice_analysis/downloads/releases/temp/isip_r00_n11_t03/util/index.html:the core speech recognition and speech processing tools.
/cavs/hse/ies/www/projects/voice_analysis/downloads/releases/temp/final_prod/isip_r00_n11_t03/class/asr/SpeechRecognizer/index.html:
- This example shows how to set up and run the speech recognizer
/cavs/hse/ies/www/projects/voice_analysis/downloads/releases/temp/final_prod/isip_r00_n11_t03/class/algo/FourierTransform/index.html:The Discrete Cosine Transform (DCT) is widely used for speech and
/cavs/hse/ies/www/projects/voice_analysis/downloads/releases/temp/final_prod/isip_r00_n11_t03/class/mmedia/index.html: and speech language model formats in this library.
/cavs/hse/ies/www/projects/voice_analysis/downloads/releases/temp/final_prod/isip_r00_n11_t03/class/mmedia/JSGFParser/index.html:and decoding in the ISIP speech recognizer. For details on JSGF, see:
/cavs/hse/ies/www/projects/voice_analysis/downloads/releases/temp/final_prod/isip_r00_n11_t03/util/index.html:the core speech recognition and speech processing tools.
/cavs/hse/ies/www/projects/voice_analysis/html/downloads.html: and network decoding for the 1249 pre-transcribed speech files
/cavs/hse/ies/www/projects/voice_analysis/images/x.html:and this is speech 4
/cavs/hse/ies/www/projects/voice_analysis/images/x.html:30 and this is to signify speech
/cavs/hse/ies/www/projects/voice_analysis/images/x.html:the speech and 387 get.
/cavs/hse/ies/www/publications/books/msstate_theses/1999/support_vectors/proposal_defense/html/svm_main.html:handle the dynamics of speech and SVMs provide us with powerful
/cavs/hse/ies/www/publications/books/msstate_theses/1999/support_vectors/proposal_defense/html/svm_main.html:Preliminary experiments on classifying speech data at the frame and
/cavs/hse/ies/www/publications/conferences/aaas/2000/speech_recognition/html/title.html:Can advances in speech recognition make spoken language as convenient and as
/cavs/hse/ies/www/publications/conferences/dod_lvcsr/2000/asr/doc/paper_final.html:The system uses a common front-end that transforms the input speech signal into mel-spaced cepstral coefficients appended with their first and second derivatives [2]. Standard features of this front-end are pre-emphasis filtering, windowing, debiasing, and energy normalization. To improve robustness to channel variations and noise, our evaluation system
/cavs/hse/ies/www/publications/conferences/dod_lvcsr/2000/asr/presentation/html/overview_00.html:
- Training, education and dissemination of information related to all aspects of speech research
/cavs/hse/ies/www/publications/conferences/dod_lvcsr/2001/transcriptions/presentation/html/title.html:In this paper, we analyze the effects of transcription errors on performance of a Hidden Markov Model (HMM) based speech recognition system. Recent experiments have shown that having a clean set of training data does not improve the performance while having more training data yields better performance. Is it possible for the current speech recognition systems to utilize the low quality transcriptions The current training algorithms used to train a system rely on proper training data. Due to human limitations, it is not possible to transcribe data perfectly and the data is bound to have some errors. Could this lead to a severe degradation in performance of the system? Experiments were performed on several databases like TIDIGITS, OGI Alphadigits and SWB to verify this effect. The performance on SWB degrades by 3.5% when the database is corrupted by 16%. There could be several factors like the algorithm used for training, number of mixture components, amount of training data that contribute to this performance and some of these factors have been addressed.
/cavs/hse/ies/www/publications/conferences/internet2/2000/job_submission/html/title.html:towards educating people about signal processing and speech
/cavs/hse/ies/www/publications/courses/ece_4512/templates/presentation/example/html/sr_02.html: speech databases and software.
/cavs/hse/ies/www/publications/courses/ece_4512/templates/presentation/example/html/sr_02.html: speech databases, software, and performance analysis.
/cavs/hse/ies/www/publications/courses/ece_4512/templates/web_site/html/abstract.html:Preliminary experiments on classifying speech data at the frame and
/cavs/hse/ies/www/publications/courses/ece_4512/templates/web_site/html/abstract.html:handle the dynamics of speech and SVMs provide us with powerful
/cavs/hse/ies/www/publications/courses/ece_4773/projects/1998/conference/dsp_00.html:in computer graphics, image classification, and speech recognition.
/cavs/hse/ies/www/publications/courses/ece_4773/projects/1998/group_signal/auxiliary/intro_pres/page_30.html: articulatory systems is applied to better model speech and
/cavs/hse/ies/www/publications/courses/ece_4773/projects/1998/group_signal/software/doc/auxiliary/intro_pres/page_30.html: articulatory systems is applied to better model speech and
/cavs/hse/ies/www/publications/courses/ece_4773/projects/main_page.html: graphics, image, and speech processing
/cavs/hse/ies/www/publications/courses/ece_7000_speech/lectures/1999/lecture_03/presentation/html/lecture_03_00_02.html: a particularly appropriate way to encode speech and the predictor parameters are a valuable source of information for recognition purposes
/cavs/hse/ies/www/publications/courses/ece_7000_speech/lectures/1999/lecture_03/presentation/html/lecture_03_00_03.html: the first researchers to directly apply linear prediction techniques to speech analysis and synthesis were Saito and Itakura[1966] and Atal and Schroeder[1967]
/cavs/hse/ies/www/publications/courses/ece_7000_speech/lectures/1999/lecture_03/presentation/html/plp_00.html: real and imaginary parts of short-term speech spectrum are squared and added to get the short-term power spectrum
/cavs/hse/ies/www/publications/courses/ece_7000_speech/lectures/1999/lecture_04/presentation/html/lecture_04_03_02.html: In a typical speech recognition system, the reference and test
/cavs/hse/ies/www/publications/courses/ece_7000_speech/lectures/1999/lecture_05/presentation/html/lecture_05_00.html:these two techniques and explore their applications in speech
/cavs/hse/ies/www/publications/courses/ece_7000_speech/lectures/1999/lecture_05/presentation/lecture_05_00.html:these two techniques and explore their applications in speech
/cavs/hse/ies/www/publications/courses/ece_7000_speech/lectures/1999/lecture_12/presentation/lecture_12_00.html:another and generate an output. In speech recognition, each acoustic
/cavs/hse/ies/www/publications/courses/ece_7000_speech/lectures/1999/lecture_13/presentation/html/lecture_13_05_01.html: - M.J.F Gales and S. Young, "An improved approach to the hidden markov model decomposition of speech and noise", In ICASSP-92, pages I-233-I-236, 1992
/cavs/hse/ies/www/publications/courses/ece_7000_speech/lectures/1999/lecture_14/presentation/html/lecture_14_03.html: - speech is a two-dimensional process with axes in frequency and
/cavs/hse/ies/www/publications/courses/ece_7000_speech/lectures/1999/lecture_14/presentation/lecture_14_00.html:between the Markov chains common to speech recognition and the
/cavs/hse/ies/www/publications/courses/ece_8443/presentations/1999/model_selection/html/ms_main_00.html:The primary problem in large vocabulary conversational speech recognition (LVCSR) is poor acoustic-level matching due to large variability in pronunciations. There is much to explore about the "quality" of states in an HMM and the inter-relationships between inter-state and intra-state Gaussians used to model speech. Of particular interest is the variable discriminating power of the individual states and its relation to the initial model topology. In this project we exploit such dependencies through model topology optimization based on the Bayesian Information Criterion (BIC).
/cavs/hse/ies/www/publications/courses/ece_8443/presentations/1999/phone_classification/html/pr_intro_00.html: development and evaluation of automatic speech recognition
/cavs/hse/ies/www/publications/courses/ece_8463/homework/2000_spring/main_page.html:Train and evaluate a context-dependent phone Switchboard speech recognition
/cavs/hse/ies/www/publications/courses/ece_8463/homework/2002_spring/hw_04/index.html:
- Upsample an 8 kHz sampled speech signal to 48 kHz and repeat
/cavs/hse/ies/www/publications/courses/ece_8463/lectures/2000_spring/html/title.html:experience teaching digital signal processing and speech recognition
/cavs/hse/ies/www/publications/courses/ece_8463/lectures/2000_spring/html/title.html:domain speech recognition system, data, toolkits, and exercises to
/cavs/hse/ies/www/publications/courses/ece_8463/lectures/2000_spring/html/title.html:and that this technology has numerous applications beyond speech recognition.
/cavs/hse/ies/www/publications/courses/ece_8463/lectures/2000_spring/lecture_04/html/lecture_04_02.html: speech waveform and spectrum.
/cavs/hse/ies/www/publications/courses/ece_8463/lectures/2002_spring/lecture_04/lecture_04_01.html: A weighting is used most often in speech research (and
/cavs/hse/ies/www/publications/courses/ece_8463/lectures/2002_spring/lecture_05/lecture_05_03.html: by imperfect analysis and modeling techniques (essential in speech
/cavs/hse/ies/www/publications/courses/ece_8463/lectures/2002_spring/lecture_06/lecture_06_00.html: of speech production physiology and linguistic models
/cavs/hse/ies/www/publications/courses/ece_8463/lectures/2002_spring/lecture_06/lecture_06_01.html: of the speech waveform and spectrum
/cavs/hse/ies/www/publications/courses/ece_8463/lectures/2002_spring/lecture_07/lecture_07_04.html: morpheme "race", but have different meanings and part of speech
/cavs/hse/ies/www/publications/courses/ece_8463/lectures/2002_spring/lecture_12/lecture_12_05.html:Types II and III are most commonly used in speech processing because
/cavs/hse/ies/www/publications/courses/ece_8463/lectures/2002_spring/lecture_12/lecture_12_09.html:a filterbank analysis of the speech signal (temporal and
/cavs/hse/ies/www/publications/courses/ece_8463/lectures/2002_spring/lecture_13/lecture_13_00.html:and information found in most standard DSP or speech textbooks:
/cavs/hse/ies/www/publications/courses/ece_8463/lectures/2002_spring/lecture_22/lecture_22_00.html:This material can be found in most speech recognition and pattern
/cavs/hse/ies/www/publications/courses/ece_8463/lectures/2002_spring/lecture_23/lecture_23_00.html:and information found in most standard speech textbooks:
/cavs/hse/ies/www/publications/courses/ece_8463/lectures/2002_spring/lecture_24/lecture_24_00.html:and information found in most standard speech textbooks:
/cavs/hse/ies/www/publications/courses/ece_8463/lectures/2002_spring/lecture_25/lecture_25_00.html:and information found in most standard speech textbooks:
/cavs/hse/ies/www/publications/courses/ece_8463/lectures/2002_spring/lecture_29/lecture_29_08.html:
- CFGs provide a natural bridge between speech recognition and
/cavs/hse/ies/www/publications/courses/ece_8463/lectures/2004_fall/lecture_04/lecture_04_01.html: A weighting is used most often in speech research (and
/cavs/hse/ies/www/publications/courses/ece_8463/lectures/2004_fall/lecture_05/lecture_05_03.html: by imperfect analysis and modeling techniques (essential in speech
/cavs/hse/ies/www/publications/courses/ece_8463/lectures/2004_fall/lecture_06/lecture_06_00.html: of speech production physiology and linguistic models
/cavs/hse/ies/www/publications/courses/ece_8463/lectures/2004_fall/lecture_06/lecture_06_01.html: of the speech waveform and spectrum
/cavs/hse/ies/www/publications/courses/ece_8463/lectures/2004_fall/lecture_07/lecture_07_04.html: morpheme "race", but have different meanings and part of speech
/cavs/hse/ies/www/publications/courses/ece_8463/lectures/2004_fall/lecture_12/lecture_12_05.html:Types II and III are most commonly used in speech processing because
/cavs/hse/ies/www/publications/courses/ece_8463/lectures/2004_fall/lecture_12/lecture_12_09.html:a filterbank analysis of the speech signal (temporal and
/cavs/hse/ies/www/publications/courses/ece_8463/lectures/2004_fall/lecture_13/lecture_13_00.html:and information found in most standard DSP or speech textbooks:
/cavs/hse/ies/www/publications/courses/ece_8463/lectures/2004_fall/lecture_22/lecture_22_00.html:This material can be found in most speech recognition and pattern
/cavs/hse/ies/www/publications/courses/ece_8463/lectures/2004_fall/lecture_23/lecture_23_00.html:and information found in most standard speech textbooks:
/cavs/hse/ies/www/publications/courses/ece_8463/lectures/2004_fall/lecture_24/lecture_24_00.