Bh. Juang et al., MINIMUM CLASSIFICATION ERROR RATE METHODS FOR SPEECH RECOGNITION, IEEE transactions on speech and audio processing, 5(3), 1997, pp. 257-265
A critical component in the pattern matching approach to speech recogn
ition is the training algorithm, which aims at producing typical (refe
rence) patterns or models for accurate pattern comparison, In this pap
er, we discuss the issue of speech recognizer training from a broad pe
rspective with root in the classical Bayes decision theory, We differe
ntiate the method of classifier design by way of distribution estimati
on and the discriminative method of minimizing classification error ra
te based on the fact that in many realistic applications, such as spee
ch recognition, the real signal distribution form is rarely known prec
isely, We argue that traditional methods relying on distribution estim
ation are suboptimal when the assumed distribution form is not the tru
e one, and that ''optimality'' in distribution estimation does not aut
omatically translate into ''optimality'' in classifier design, We comp
are the two different methods in the context of hidden Markov modeling
for speech recognition, We show the superiority of the minimum classi
fication error (MCE) method over the distribution estimation method by
providing the results of several key speech recognition experiments,
In general, the MCE method provides a significant reduction of recogni
tion error rate.