This paper presents a new speech recognition framework towards fulfilling optimal Bayes decision theory, which is essential for general pattern recognition. The recognition procedure is developed through minimizing the Bayes risk, or equivalently the expected loss due to classification action. Typically, loss function measures the penalty/evidence of choosing a candidate hypothesis. This function was manually specified or empirically calculated. Here, we exploit a novel Bayes loss function via testing the hypotheses whether the classification action produces loss or not. A Bayes factor is derived to measure loss in a statistical and meaningful way. Attractively, Bayes loss function using predictive distributions is robust to the uncertainty of environments. Also, optimizing this Bayes criterion equals to minimizing classification errors of test data. The relation between the minimum classification error (MCE) classifier and the proposed optimal Bayes classifier (OBC) is bridged. Specifically, the logarithm of Bayes factor in OBC is analogous to the misclassification measure in MCE when using predictive distribution as the discriminant function. We accordingly build a robust and discriminative classification for large vocabulary continuous speech recognition. In the experiments on broadcast news transcription, the new OBC rule significantly outperforms traditional maximum a posteriori classification.

We present a new discriminative linear regression adaptation algorithm for hidden Markov model (HMM) based speech recognition. The cluster-dependent regression matrices are estimated from speaker-specific adaptation data through maximizing the aggregate a posteriori probability, which can be expressed in a form of classification error function adopting the logarithm of posterior distribution as the discriminant function. Accordingly, the aggregate a posteriori linear regression (AAPLR) is developed for discriminative adaptation where the classification errors of adaptation data are minimized. Because the prior distribution of regression matrix is involved, AAPLR is geared with the Bayesian learning capability. We demonstrate that the difference between AAPLR discriminative adaptation and maximum a posteriori linear regression (MAPLR) adaptation is due to the treatment of the evidence. Different from minimum classification error linear regression (MCELR), AAPLR has closed-form solution to fulfil rapid adaptation. Experimental results reveal that AAPLR speaker adaptation does improve speech recognition performance with moderate computational cost compared to maximum likelihood linear regression (MLLR), MAPLR, MCELR and conditional maximum likelihood linear regression (CMLLR). These results are verified for supervised adaptation as well as unsupervised adaptation for different numbers of adaptation data.

In this paper, we present a rapid and discriminative speaker adaptation algorithm for speech recognition. The adaptation paradigm is constructed under the popular linear regression transformation framework. Attractively, we estimate the regression matrices from the speaker-specific adaptation data according to the aggregate a posteriori criterion, which can be expressed in a form of classification error function. The goal of the proposed aggregate a posteriori linear regression (AAPLR) is to estimate the discriminative linear regression matrices for transformation-based adaptation so that the classification errors can be minimized. Different from minimum classification error linear regression (MCELR), the AAPLR algorithm has a closed-form solution to achieve rapid speaker adaptation. The experimental results reveal that AAPLR speaker adaptation does improve speech recognition performance with moderate computational cost compared to the maximum likelihood linear regression (MLLR), maximum a posteriori linear regression (MAPLR) and MCELR.

We present Bayesian duration modeling and learning for speech recognition under nonstationary speaking rates and noise conditions. In this study, the Gaussian, Poisson and gamma distributions are investigated, to characterize duration models. The maximum a posteriori (MAP) estimate of the gamma duration model is developed. To exploit the sequential learning, we adopt the Poisson duration model, incorporated with gamma prior density, which belongs to the conjugate prior family. When the adaptation data are sequentially observed, the gamma posterior density is produced for twofold advantages. One is to determine the optimal quasi-Bayes (QB) duration parameter, which can be merged in HMM's for speech recognition. The other one is to build the updating mechanism of gamma prior statistics for sequential learning. An expectation-maximization algorithm is applied to fulfill parameter estimation. In the experiments, the proposed Bayesian approaches significantly improve the speech recognition performance of Mandarin broadcast news. Batch and sequential learning are investigated for MAP and QB duration models, respectively.

This paper presents the Bayesian speech duration modeling and learning for hidden Markov model (HMM) based speech recognition. We focus on the sequential learning of HMM state duration using quasi-Bayes (QB) estimate. The adapted duration models are robust to nonstationary speaking rates and noise conditions. In this study, the Gaussian, Poisson, and gamma distributions are investigated to characterize the duration models. The maximum a posteriori (MAP) estimate of gamma duration model is developed. To exploit the sequential learning, we adopt the Poisson duration model incorporated with gamma prior density, which belongs to the conjugate prior family. When the adaptation data are sequentially observed, the gamma posterior density is produced with twofold advantages. One is to determine the optimal QB duration parameter, which can be merged in HMMs for speech recognition. The other one is to build the updating mechanism of gamma prior statistics for sequential learning. EM algorithm is applied to fulfill QB parameter estimation. The adaptation of overall HMM parameters can be performed simultaneously. In the experiments, the proposed adaptive duration model improves the speech recognition performance of Mandarin broadcast news and noisy connected digits. The batch and sequential learning are respectively investigated for MAP and QB duration models.

A decision tree is built by successively splitting the observation frames of a phonetic unit according to the best phonetic questions. To prevent over-large tree models, the stopping criterion is required to suppress tree growing. It is crucial to exploit the goodness-of-split criteria to choose the best questions for node splitting and test if the hypothesis of splitting should be terminated. The robust tree models could be established. In this study, we apply the Hubert's Γ statistic as the node splitting criterion and the T2-statistic as the stopping criterion. Hubert's Γ statistic is a cluster validity measure, which characterizes the degree of clustering in the available data. This measure is useful to select the best questions to unravel tree nodes. Further, we examine the population closeness of two child nodes with a significant level, T2-statistic is determined to validate whether the corresponding mean vectors are close together. The splitting is stopped when validated. In continuous speech recognition experiments, the proposed methods achieve better recognition rates with smaller tree models compared to the maximum likelihood and minimum description length criteria.

This paper presents an online/sequential linear regression adaptation framework for hidden Markov model (HMM) based speech recognition. Our attempt is to sequentially improve the speaker-independent (SI) speech recognizer to meet nonstationary environments via linear regression adaptation of SI HMMs. A quasi-Bayes linear regression (QBLR) algorithm is developed to execute online adaptation where the regression matrix is estimated using QB theory. In the estimation, we moderately specify the prior density of the regression matrix as a matrix variate normal distribution and exactly derive the pooled posterior density belonging to the same distribution family. Accordingly, the optimal regression matrix can be easily calculated. Also, the reproducible prior/posterior density pair provides a meaningful mechanism for sequential learning of prior statistics. At each sequential epoch, only the updated prior statistics and the current observed data are required for adaptation. In general, the proposed QBLR is universal and can be reduced to the well-known maximum likelihood linear regression (MLLR) and maximum a posteriori linear regression (MAPLR). Experiments show that the QBLR is effective for speaker adaptation in car environments.