We have made significant progress in automatic speech recognition (ASR) for well-defined applications like dictation and medium vocabulary transaction processing tasks in relatively controlled environments. However, ASR performance has yet to reach the level required for speech to become a truly pervasive user interface. Indeed, even in “clean ” acoustic environments, and for a variety of tasks, state of the art ASR system

This paper investigates the use of maximum likelihood linear regression (MLLR) for both speaker and environment adaptation. MLLR transforms the mean and variance parameters of a set of HMMs. In this paper a number of different types of linear transformations of the variances are examined including full, block diagonal, and diagonal transformation matrices. Experiments on large vocabulary speaker independent data sets are described. On all the data sets examined the use of MLLR mean and variance compensation reduced the error rate compared to mean-only compensation. Furthermore, the use of a block diagonal or full transformation of the variances on the clean data task showed slight improvements over the diagonal case. However, when some environmental mismatch was present there was no difference in performance between using multiple diagonal variance transformations and a more complex single variance transform.

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

Hidden Markov models (HMMs) for automatic speech recognition rely on high dimensional feature vectors to summarize the short-time properties of speech. Correlations between features can arise when the speech signal is non-stationary or corrupted by noise. We investigate how to model these correlations using factor analysis, a statistical method for dimensionality reduction. Factor analysis uses a small number of parameters to model the covariance structure of high dimensional data. These parameters can be chosen in two ways: (i) to maximize the likelihood of observed speech signals, or (ii) to minimize the number of classification errors. We derive an Expectation-Maximization (EM) algorithm for maximum likelihood estimation and a gradient descent algorithm for improved class discrimination. Speech recognizers are evaluated on two tasks, one small-sized vocabulary (connected alpha-digits) and one medium-sized vocabulary (New Jersey town names). We find that modeling feature correlations...

The last decade has witnessed substantial progress in speech recognition technology, with todays state-of-the-art systems being able to transcribe unrestricted broadcast news audio data with a word error of about 20%.

This paper gives an overview of automatic speaker recognition technology, with an emphasis on text-independent recognition. Speaker recognition has been studied actively for several decades. We give an overview of both the classical and the state-of-the-art methods. We start with the fundamentals of automatic speaker recognition, concerning feature extraction and speaker modeling. We elaborate advanced computational techniques to address robustness and session variability. The recent progress from vectors towards supervectors opens up a new area of exploration and represents a technology trend. We also provide an overview of this recent development and discuss the evaluation methodology of speaker recognition systems. We conclude the paper with discussion on future directions.

Real-life speaker verification systems are often implemented using client model adaptation methods, since the amount of data available for each client is often too low to consider plain Maximum Likelihood methods. While the Bayesian Maximum A Posteriori (MAP) adaptation method is commonly used in speaker verification, other methods have proven to be successful in related domains such as speech recognition. This paper reports on experimental comparison between three well-known adaptation methods, namely MAP, Maximum Likelihood Linear Regression, and finally Eigen-Voices. All three methods are compared to the more classical Maximum Likelihood method, and results are given for a subset of the 1999 NIST Speaker Recognition Evaluation database. 1.

This paper describes connectionist techniques for recognition of Broadcast News. The fundamental difference between connectionist systems and more conventional mixture-of-Gaussian systems is that connectionist models directly estimate posterior probabilities as opposed to likelihoods. Access to posterior probabilities has enabled us to develop a number of novel approaches to confidence estimation, pronunciation modelling and search. In addition we have investigated a new feature extraction technique based on the modulation-filtered spectrogram (MSG), and methods for combining multiple information sources. We have incorporated all of these techniques into a system for the transcription

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