This paper examines the application of linear transformations for speaker and environmental adaptation in an HMM-based speech recognition system. In particular, transformations that are trained in a maximum likelihood sense on adaptation data are investigated. Other than in the form of a simple bias, strict linear feature-space transformations are inappropriate in this case. Hence, only model-based linear transforms are considered. The paper compares the two possible forms of model-based transforms: (i) unconstrained, where any combination of mean and variance transform may be used, and (ii) constrained, which requires the variance transform to have the same form as the mean transform (sometimes referred to as feature-space transforms). Re-estimation formulae for all appropriate cases of transform are given. This includes a new and efficient "full" variance transform and the extension of the constrained model-space transform from the simple diagonal case to the full or block-diagonal case. The constrained and unconstrained transforms are evaluated in terms of computational cost, recognition time efficiency, and use for speaker adaptive training. The recognition performance of the two model-space transforms on a large vocabulary speech recognition task using incremental adaptation is investigated. In addition, initial experiments using the constrained model-space transform for speaker adaptive training are detailed. 1 The author is now at the IBM T.J. Watson Research Center, Yorktown Heights, NY 10598, USA 1

Graphical techniques for modeling the dependencies of random variables have been explored in a variety of di erent areas including statistics, statistical physics, arti-cial intelligence, speech recognition, image processing, and genetics. Formalisms for manipulating these models have been developed relatively independently in these research communities. In this paper we explore hidden Markov models (HMMs) and related structures within the general framework of probabilistic independence networks (PINs). The paper contains a self-contained review of the basic principles of PINs. It is shown that the well-known forward-backward (F-B) and Viterbi algorithms for HMMs are special cases of more general inference algorithms for arbitrary PINs. Furthermore, the existence of inference and estimation algorithms for more general graphical models provides a set of analysis tools for HMM practitioners who wish to explore a richer class of HMM structures. Examples of relatively complex models to handle sensor fusion and coarticulation in speech recognition are introduced and treated within the graphical model framework to illustrate the advantages of the general approach. 1

One of the key issues for adaptation algorithms is to modify a large number of parameters with only a small amount of adaptation data. Speaker adaptation techniques try to obtain near speaker dependent (SD) performance with only small amounts of speaker specific data, and are often based on initial speaker independent (SI) recognition systems. Some of these speaker adaptation techniques may also be applied to the task of adaptation to a new acoustic environment. In this case a SI recognition system trained in, typically, a clean acoustic environment is adapted to operate in a new, noise-corrupted, acoustic environment. This paper examines the Maximum Likelihood Linear Regression (MLLR) adaptation technique. MLLR estimates linear transformations for groups of models parameters to maximise the likelihood of the adaptation data. Previously, MLLR has been applied to the mean parameters in mixture Gaussian HMM systems. In this paper MLLR is extended to also update the Gaussian variances and re-estimation formulae are derived for these variance transforms. MLLR with variance compensation is evaluated on several large vocabulary recognition tasks. The use of mean and variance MLLR adaptation was found to give an additional 2% to 7% decrease in word error rate over mean-only MLLR adaptation. 1

Maximum likelihood linear regression (MLLR) is an adaptation technique suitable for both speaker and environmental model-based adaptation. The models are adapted using a set of linear transformations, estimated in a maximum likelihood fashion from the available adaptation data. As these transformations can capture general relationships between the original model set and the current speaker, or new acoustic environment, they can be effective in adapting all the HMM distributions with limited adaptation data. Two important decisions that must be made are (i) how to cluster components together, such that they all have a similar transformation matrix, and (ii) how many transformation matrices to generate for a given block of adaptation data. This paper addresses both problems. Firstly it describes two optimal clustering techniques, in the sense of maximising the likelihood of the adaptation data. The first assigns each component to one of the regression classes. This may be used to generat...

When performing speaker adaptation there are two conicting requirements. First the transform must be powerful enough to represent the speaker. Second the transform must be quickly and easily estimated for any particular speaker. The most popular adaptation schemes have used many parameters to adapt the models to be representative of an individual speaker. This limits how rapidly the models may be adapted to a new speaker or acoustic environment. This paper examines an adaptation scheme requiring very few parameters, cluster adaptive training (CAT). CAT may be viewed as a simple extension to speaker clustering. Rather than selecting a single cluster as representative of a particular speaker, a linear interpolation of all the cluster means is used as the mean of the particular speaker. This scheme naturally falls into an adaptive training framework. Maximum likelihood estimates of the interpolation weights are given. Furthermore, simple re-estimation formulae for cluster means, represented both explicitly and by sets of transforms of some canonical mean, are given. On a speakerindependent task CAT reduced the word error rate using very little adaptation data. In addition when combined with other adaptation schemes it gave a 5% reduction in word error rate over adapting a speaker-independent model set. 2 1

A speaker adaptation strategy is described that is based on finding a subset of speakers, from the training set, who are acoustically close to the test speaker, and using only the data from these speakers (rather than the complete training corpus) to re-estimate the system parameters. Further, a linear transformation is computed for every one of the selected training speakers to better map the training speaker's data to the test speaker's acoustic space. Finally, the system parameters (Gaussian means) are re-estimated specifically for the test speaker using the transformed data from the selected training speakers. Experiments showed that this scheme is capable of reducing the error rate by 10-15% with the use of as little as 3 sentences of adaptation data.

We extend our previously proposed quasi-Bayes adaptive learning framework to cope with the correlated continuous density hidden Markov models 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 on-line speaker adaptation application, the algorithm is experimentally shown to be asymptotic 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 models. 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.

This paper presents three forms of linear transform based speaker adaptation that can give better performance than standard maximum likelihood linear regression (MLLR) adaptation. For unsupervised adaptation, a lattice-based technique is introduced which is compared to MLLR using confidence scores. For supervised adaptation, estimation of the adaptation matrices using the maximum mutual information criterion is discussed which leads to the MMILR approach. Recognition experiments show that lattice MLLR can reduce word error rates on a Switchboard task by 1.4% absolute. For recognition of non-native speech from the Wall Street Journal database, a reduction in word error rate of 10-16% relative was obtained using MMILR compared to standard MLLR.

Currently the most popular acoustic model for speech recognition is the hidden Markov model (HMM). However, HMMs are based on a series of assumptions some of which are known to be poor. In particular, the assumption that successive speech frames are conditionally independent given the discrete state that generated them is not a good assumption for speech recognition. State space models may be used to address some shortcomings of this assumption. State space models are based on a continuous state vector evolving through time according to a state evo-

Automatic name dialing is a practical and interesting application of speech recognition on telephony systems. The IBM name recognition system is a large vocabulary, speaker independent system currently in use for reaching IBM employees in the United States. In this paper, we present some innovative algorithms that improve name recognition accuracy. Unlike transcription tasks, such as the Switchboard task, recognition of names poses a variety of different problems. Several of these problems arise from the fact that foreign names are hard to pronounce for speakers who are not familiar with the names and that there are no standardized methods for pronouncing proper names. Noise robustness is another very important factor as these calls are typically made in noisy environments, such as from a car, cafeteria, airport, etc. and over different kinds of cellular and land-line telephone channels. We have performed a systematic analysis of the speech recognition errors and tackled the issues separately with techniques ranging from weighted speaker clustering, massive adaptation, rapid and unsupervised adaptation methods to pronunciation modeling methods. We find that the decoding accuracy can be improved significantly (28 % relative) in this manner. 1.