On-line adaptive learning of the continuous density hidden Markov model based on approximate recursive Bayes estimate

We present a framework of quasi-Bayes (QB) learning of the parameters of the continuous density hidden Markov model (CDHMM) with Gaussian mixture state observation densities. The QB formulation is based on the theory of recursive Bayesian inference. The QB algorithm is designed to incrementally update the hyperparameters of the approximate posterior distribution and the CDHMM parameters simultaneously. By further introducing a simple forgetting mechanism to adjust the contribution of previously observed sample utterances, the algorithm is adaptive in nature and capable of performing an online adaptive learning using only the current sample utterance. It can, thus, be used to cope with the time-varying nature of some acoustic and environmental variabilities, including mismatches caused by changing speakers, channels, and transducers. As an example, the QB learning framework is applied to on-line speaker adaptation and its viability is confirmed in a series of comparative experiments using a 26-letter English alphabet vocabulary.published_or_final_versio

On-line adaptive learning of the correlated continuous density hidden Markov models for speech recognition

We extend our previously proposed quasi-Bayes adaptive learning framework to cope with the correlated continuous density hidden Markov models (HMMs) with Gaussian mixture state observation densities in which all mean vectors are assumed to be correlated and have a joint prior distribution. A successive approximation algorithm is proposed to implement the correlated mean vectors' updating. As an example, by applying the method to an on-line speaker adaptation application, the algorithm is experimentally shown to be asymptotically convergent as well as being able to enhance the efficiency and the effectiveness of the Bayes learning by taking into account the correlation information between different model parameters. The technique can be used to cope with the time-varying nature of some acoustic and environmental variabilities, including mismatches caused by changing speakers, channels, transducers, environments, and so on.published_or_final_versio

On adaptive decision rules and decision parameter adaptation for automatic speech recognition

Recent advances in automatic speech recognition are accomplished by designing a plug-in maximum a posteriori decision rule such that the forms of the acoustic and language model distributions are specified and the parameters of the assumed distributions are estimated from a collection of speech and language training corpora. Maximum-likelihood point estimation is by far the most prevailing training method. However, due to the problems of unknown speech distributions, sparse training data, high spectral and temporal variabilities in speech, and possible mismatch between training and testing conditions, a dynamic training strategy is needed. To cope with the changing speakers and speaking conditions in real operational conditions for high-performance speech recognition, such paradigms incorporate a small amount of speaker and environment specific adaptation data into the training process. Bayesian adaptive learning is an optimal way to combine prior knowledge in an existing collection of general models with a new set of condition-specific adaptation data. In this paper, the mathematical framework for Bayesian adaptation of acoustic and language model parameters is first described. Maximum a posteriori point estimation is then developed for hidden Markov models and a number of useful parameters densities commonly used in automatic speech recognition and natural language processing.published_or_final_versio

Efficient training algorithms for HMMs using incremental estimation

Typically, parameter estimation for a hidden Markov model (HMM) is performed using an expectation-maximization (EM) algorithm with the maximum-likelihood (ML) criterion. The EM algorithm is an iterative scheme that is well-defined and numerically stable, but convergence may require a large number of iterations. For speech recognition systems utilizing large amounts of training material, this results in long training times. This paper presents an incremental estimation approach to speed-up the training of HMMs without any loss of recognition performance. The algorithm selects a subset of data from the training set, updates the model parameters based on the subset, and then iterates the process until convergence of the parameters. The advantage of this approach is a substantial increase in the number of iterations of the EM algorithm per training token, which leads to faster training. In order to achieve reliable estimation from a small fraction of the complete data set at each iteration, two training criteria are studied; ML and maximum a posteriori (MAP) estimation. Experimental results show that the training of the incremental algorithms is substantially faster than the conventional (batch) method and suffers no loss of recognition performance. Furthermore, the incremental MAP based training algorithm improves performance over the batch versio

On-line adaptation of the SCHMM parameters based on the segmental quasi-bayes learning for speech recognition

On-line quasi-Bayes adaptation of the mixture coefficients and mean vectors in semicontinuous hidden Markov model (SCHMM) is studied. The viability of the proposed algorithm is confirmed and the related practical issues are addressed in a specific application of on-line speaker adaptation using a 26-word English alphabet vocabulary.published_or_final_versio

Online adaptive learning of continuous-density hidden Markov models based on multiple-stream prior evolution and posterior pooling

We introduce a new adaptive Bayesian learning framework, called multiple-stream prior evolution and posterior pooling, for online adaptation of the continuous density hidden Markov model (CDHMM) parameters. Among three architectures we proposed for this framework, we study in detail a specific two stream system where linear transformations are applied to the mean vectors of the CDHMMs to control the evolution of their prior distribution. This new stream of prior distribution can be combined with another stream of prior distribution evolved without any constraints applied. In a series of speaker adaptation experiments on the task of continuous Mandarin speech recognition, we show that the new adaptation algorithm achieves a similar fast-adaptation performance as that of the incremental maximum likelihood linear regression (MLLR) in the case of small amount of adaptation data, while maintains the good asymptotic convergence property as that of our previously proposed quasi-Bayes adaptation algorithms.published_or_final_versio