Automatic determination of acoustic model topology using variational Bayesian estimation and clustering

We describe the automatic determination of an acoustic model for speech recognition, which is very complicated and includes latent variables, using VBEC: variational Bayesian estimation and clustering for speech recognition. We propose an efficient Gaussian mixture model (GMM) based phonetic decision tree construction within the VBEC framework. The proposed method features a novel approach to reduce the unrealistically large number of computations needed for iterative calculations in the GMM-based decision tree method to a practical level by assuming that each Gaussian per state has the same occupancy and is represented by the same posterior distribution for the covariance parameter. The experimental results confirmed that VBEC automatically provided an optimum model topology with the highest performance level.