Biing-Hwang Juang

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A critical component in the pattern matching approach to speech recognition is the training algorithm, which aims at producing typical (reference) patterns or models for accurate pattern comparison. In this paper, we discuss the issue of speech recognizer training from a broad perspective with root in the classical Bayes decision theory. We differentiate(More)
It is generally agreed that, for a given speech recognition task, a speaker-dependent system usually outperforms a speaker-independent system, as long as a sufficient amount of training data is available. When the amount of speaker-specific training data is limited, however, such a performance gain is not guaranteed. One way to improve the performance is to(More)
In this paper a signal modeling technique based upon finite mixture autoregressive probabilistic functions of Markov chains is developed and applied to the problem of speech recognition, particularly speaker-independent recognition of isolated digits. Two types of mixture probability densities are investigated: finite mixtures of Gaussian autoregressive(More)
This paper proposes a statistical model-based speech dereverberation approach that can cancel the late reverberation of a reverberant speech signal captured by distant microphones without prior knowledge of the room impulse responses. With this approach, the generative model of the captured signal is composed of a source process, which is assumed to be a(More)