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

In our previous works, a Segmental Switching Linear Gaussian Hidden Markov Model (SSLGHMM) was proposed to model "noisy" speech utterance for robust speech recognition. Both ML (maximum likelihood) and MCE (minimum classification error) training procedures were developed for training model parameters and their effectiveness was confirmed by evaluation experiments on Aurora2 and Aurora3 databases. In this paper, we present an ML approach to unsupervised online adaptation (OLA) of SSLGHMM parameters for achieving further performance improvement. An important implementation issue of how to initialize the switching linear Gaussian model parameters is also studied. Evaluation results on Finnish Aurora3 database show that in comparison with the performance of a baseline system based on ML-trained SSLGHMMs, unsupervised OLA yields a relative word error rate reduction of 4.3%, 9.1%, and 17.8% for well-matched, medium-mismatched, and high-mismatched conditions respectively.

A conventional feature compensation module for robust automatic speech recognition is usually designed separately from the training of hidden Markov model (HMM) parameters of the recognizer, albeit a maximum-likelihood (ML) criterion might be used in both designs. In this paper, we present an environment-compensated minimum classification error (MCE) training approach for the joint design of the feature compensation module and the recognizer itself. The feature compensation module is based on a stochastic vector mapping function whose parameters have to be learned from stereo data in a previous approach called SPLICE. In our proposed MCE joint design approach, by initializing the parameters with an approximate ML training procedure, the requirement of stereo data can be removed. By evaluating the proposed approach on Auroral connected digits database, a digit recognition error rate, averaged on all three test sets, of 5.66% is achieved for multicondition training. In comparison with the performance achieved by the baseline system using ETSI advanced front-end, our approach achieves an additional overall error rate reduction of 12.4%

Several recent approaches for robust speech recognition are developed based on the concept of stochastic vector mapping (SVM) that perform a frame-dependent bias removal to compensate for environmental variabilities in both training and recognition stages. Some of them require stereo recordings of both clean and noisy speech for the estimation of SVM function parameters. In this paper, we present a detailed formulation of a maximum likelihood training approach for the joint design of SVM function parameters and HMM parameters of a speech recognizer that does not rely on the availability of stereo training data. Its learning behavior and effectiveness is demonstrated by using the experimental results on the Aurora3 Finnish connected digits database recorded by using both close-talking and hands-free microphones in cars.

We have witness several works of considering the uncertainty of feature compensation module for robust speech recognition. In most of these studies, the modelling and the exploiting of the uncertainty are seldom treated in a unified way. In this paper, we present a new framework, which casts the problem of considering the uncertainty of feature compensation module as the one of designing a new discriminant function, thus the uncertainty parameters of the feature compensation module and other parameters of the discriminant function can be estimated jointly under a consistent criterion of minimum classification error (MCE). It is hoped that such MCE-trained discriminant function can improve the performance of a maximum discriminant function based speech recognition system. The preliminary experimental results on Aurora2 multi-condition tasks have confirmed the above conjecture.

In our previous works, a Switching Linear Gaussian Hidden Markov Model (SLGHMM) and its segmental derivative, SSLGHMM, were proposed to cast the problem of modelling a noisy speech utter-ance by a well-designed dynamic Bayesian network. We presented parameter learning procedures for both models with maximum likelihood (ML) criterion. The effectiveness of such models was confirmed by evaluation experiments on Aurora2 database. In this paper, we present a study of minimum classification error (MCE) training for SSLGHMM and discuss its relation to our ear-lier proposals based on stochastic vector mapping. An important implementation issue of SSLGHMM, namely the specification of switching states for a given utterance, is also studied. New evalua-tion results on Aurora3 database show that MCE-trained SSLGH-MMs achieve a relative error reduction of 21 % over a baseline sys-tem based on ML-trained continuous density HMMs (CDHMMs). 1.

In this paper, we present a formulation of minimum classification error linear regression (MCELR) for adaptation of Gaussian mixture continuous density HMM (CDHMM) parameters. We demonstrate that the MCELR can be used to adapt the MCE-trained HMM parameters under a consistent criterion. In a supervised speaker adaptation application, we observe that such adapted models perform better than the ones adapted using MLLR from the ML-trained seed models. We also observe that the MCELR performs consistently better than the MLLR for either sets of seed models.