Background Information: Dirichlet Distributions and Processes Frigyik, B., Kapila, A., & Gupta, M. (2010). Introduction to the Dirichlet Distribution and Related Processes. Blei, D. M., & Jordan, M. I. (2006). Variational Inference for Dirichlet Process Mixtures. Teh, Y. W., Dirichlet Processes: Tutorial and Practical Course. Variational Inference Kurihara, K., Welling, M., & Vlassis, N. (2006). Accelerated Variational Dirichlet Process Mixtures. Kurihara, K., Welling, M., & Teh, Y. W. (2007). Collapsed Variational Dirichlet Process Mixture Models. Eisner, J. (2011). High-Level Explanation of Variational Inference. Automatic Phone Alignment Generation: Hidden Markov Model Toolkit (HTK). All automatically generated alignments were created using HTK. The experimental setup below is based on using this software. Experimental Setup. This contains information pertaining to the lexicons used, feature extraction and training configurations, data, lists, and the automatic alignments generated for our classification algorithms. Matlab Tools: Kurihara's Variational Inference Toolkit. All of the variational methods proposed in this work require this toolkit. Neural network (NN), random forest (RF), K nearest neighbor (KNN), and Gaussian mixture model (GMM) classification setups. These are the setups of the baseline systems used to compare the performance of the variational methods to. AVDPM, CVSB, and CDP classification setups. These are the setups of AVDPM, CVSB, and CDP used in this work. These measure error rates, computation times, and the number of mixture components per label.