Application of variational Bayesian PCA for speech feature extraction

In a standard mel-frequency cepstral coefficient-based speech recognizer, it is common to use the same feature dimension and the number of Gaussian mixtures for all subunits. We proposed to use different transformations and different number of mixtures for each subunit. We obtained the transformations from mel-frequency band energies by using the variational Bayesian principal component analysis (PCA) method. In the method, hyperparameters of the Gaussian mixtures and the number of mixtures are automatically learned through maximization of a lower bound of the evidence instead of the likelihood in the conventional maximum likelihood paradigm. Analyzing the TIMIT speech data, we revealed intrinsic structures of vowels and consonants. We demonstrated the usefulness of the method for speech recognition by performing phoneme classification of /b/, /d/ and /g/ phonemes.