International Symposium on Chinese Spoken Language Processing (ISCSLP 2002)

Taipei, Taiwan
August 23-24, 2002

A Comparative Study of Quickprop and GPD Optimization Algorithms for MCELR Adaptation of CDHMM Parameters

Jian Wu, Qiang Huo

Department of Computer Science and Information Systems, The University of Hong Kong, Hong Kong

In our previous work, we have presented an approach of minimum classification error linear regression (MCELR) for adaptation of Gaussian mixture continuous density HMM (CDHMM) parameters. It is shown that a stochastic approximation approach known as the GPD (generalized probabilistic descent) can be used to optimize the MCE objective function. However, it is relatively diffi- cult to set an appropriate value for the learning control parameter to achieve a fast yet stable GPD optimization process. In this paper, we study another batch-mode approximate second-order optimization approach, namely Quickprop, aiming at speeding up the convergence of the objective function of MCELR while making the learning more robust. It is demonstrated by a series of experiments for supervised speaker adaptation that Quickprop is a better alternative to GPD for MCELR.

Full Paper

Bibliographic reference.  Wu, Jian / Huo, Qiang (2002): "A comparative study of quickprop and GPD optimization algorithms for MCELR adaptation of CDHMM parameters", In ISCSLP 2002, paper 23.