Although speech recognition has been a well studed field over the past few decades, it can be difficult to find tutorials that offer clear and useful insight into the actual process of running a task. This website aims to provide resources for new researchers to begin working on speech recognition experiments.

There are several different open-source speech recognisers available to the public. ISIP, Carnegie Melon's Sphinx, and Cambridge's HTK are some of the most popularly used systems. For our intents and purposes these tutorials focus on using HTK since a fair amount of documentation exists and there are additional resources available should someone running these tutorials have problems.

As the figure to the right shows, speech recognition can be broken down into a few key processes. In these tutorials we'll focus on three key steps: data preparation, acoustic modeling (i.e. training), and decoding (i.e. obtainin results).

• Data Preparation: Before doing anything, you must first ensure everything is in the correct format and that you have all of the proper files for your experiment. In this phase, you'll accomplish a few things including building a language model for your task, converting your audio files into features to train acoustic models, building a lexicon (i.e. reducing your dictionary to a list of used words), generating lists of monophones or triphones that exist in your data, and creating transcription files that are needed to both train acoustic models and also later for decoding your results. It's worth noting that the acoustic features mentioned above are most often vectors of Mel Frequency Cepstral Coefficients (MFCC's).
  
  
• Acoustic Modeling / Training: Once we have everything in the right format, we can begin to train our acoustic models. The most common graphical structure for these are Hidden Markov Models (HMM's). To train these acoustic models we use a multivariate Gaussian distribution where each variable is represented by one of the MFCC features. HTK uses the Baum-Welch algorithm (i.e. similar to the EM algorithm) to estimate and maximize the means and covariances of the models. Typically researchers expand these distributions to incorporate Gaussian mixture models as well.
  
  If you're not comfortable with some of this terminology, Lawrence Rabiner has written a very useful explanation in A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition.
  
  
• Decoding: After we've finished training our HMM acoustic models, we use them to generate a set of transcriptions for our testing data. We compare the generated results to the actual transcriptions to determine the word error rate (WER) of the system.