Speech feature analysis using variational Bayesian PCA

doi:10.1109/LSP.2003.810017

Return

Speech feature analysis using variational Bayesian PCA

2003-05-01

PRE

Oh‐Wook Kwon

Chan, KL

Te‐Won Lee

DOI：10.1109/LSP.2003.810017

Original

Original request for help

Save

Abstract

En 中文

In most hidden Markov model-based automatic speech recognition systems, one of the fundamental questions is to determine the intrinsic speech feature dimensionality and the number of clusters used in the Gaussian mixture model. We analyzed mel-frequency band energies using a variational Bayesian principal, component analysis method to estimate the feature dimensionality as well as the number of Gaussian mixtures by learning a maximum lower, bound of the evidence instead of maximizing the likelihood function as used in conventional speech recognition systems. In analyzing the Texas Instruments/Massachusetts Institute of Technology (TIMIT) speech database, our method revealed the intrinsic structures of vowels and consonants. The usefulness of this method is demonstrated in the superior classification performance for the most difficult phonemes /b/, /d/, and /g/.

Keywords:

phoneme classification

speech analysis

speech recognition

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.