A study of minimum classification error (MCE) linear regression for supervised adaptation of MCE-trained continuous-density hidden Markov models

作者：Wu Jian^*; Huo Qiang

来源：IEEE Transactions on Audio Speech and Language Processing, 2007, 15(2): 478-488.

DOI：10.1109/TASL.2006.881692

• hidden Markov model (HMM)
• HMM adaptation
• minimum classification error linear regression (MCELR)
• speaker adaptation

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.

• 出版日期2007-2
• 单位Microsoft