A Study of Minimum Classification Error (MCE) Linear Regression for Supervised Adaptation of MCE-Trained Continuous-Density Hidden Markov Models

Authors:
J. Wu;Q. Huo
Affiliations:
Dept. of Comput. Sci., Hong Kong Univ.;-
Venue:
IEEE Transactions on Audio, Speech, and Language Processing
Year:
2007

Citing 0
Cited 3

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Pattern Recognition Letters
A discriminative linear regression approach to adaptation of multi-prototype based classifiers and its applications for Chinese OCR

Pattern Recognition

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Abstract

In this paper, we present a formulation of minimum classification error linear regression (MCELR) for the adaptation of Gaussian mixture continuous-density hidden Markov model (CDHMM) parameters. Two optimization approaches, namely generalized probabilistic descent (GPD) and Quickprop are studied and compared for the optimization of the MCELR objective function. The effectiveness of the proposed MCELR technique is confirmed via a series of supervised speaker adaptation experiments on a task of continuous Putonghua (Mandarin Chinese) speech recognition