Speech Trajectory Discrimination using the Minimum Classification Error Learning

R. Chengalvarayan and Li Deng

Abstract

In this paper, we extend the maximum likelihood

(ML) training algorithm to the minimum classification error

(MCE) training algorithm for discriminatively estimating the

state-dependent polynomial coefficients in the stochastic trajectory

model or the trended hidden Markov model (HMM)

originally proposed in [2]. The main motivation of this extension

is the new model space for smoothness-constrained, state-bound

speech trajectories associated with the trended HMM, contrasting

the conventional, stationary-state HMM, which describes only

the piecewise-constant “degraded trajectories” in the observation

data. The discriminative training implemented for the trended

HMM has the potential to utilize this new, constrained model

space, thereby providing stronger power to disambiguate the

observational trajectories generated from nonstationary sources

corresponding to different speech classes. Phonetic classification

results are reported which demonstrate consistent performance

improvements with use of the MCE-trained trended HMM both

over the regular ML-trained trended HMM and over the MCEtrained

stationary-state HMM.

Details

Publication typeArticle
Published inIEEE Trans. on Speech and Audio Processing
Pages505-515
Volume6
Number6
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