Titlebook: Automatic Speech and Speaker Recognition; Advanced Topics Chin-Hui Lee,Frank K. Soong,Kuldip K. Paliwal Book 1996 Kluwer Academic Publisher

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An Overview of Speaker Recognition Technology,ecognition can be divided in two ways: (a) speaker identification and verification, and (b) text-dependent and text-independent methods. The second part of the paper is devoted to discussion of more specific topics of recent interest which have led to interesting new approaches and techniques. They

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Bayesian Adaptive Learning and Map Estimation of HMM,orithms are then developed for hidden Markov models and for a number of useful parametric densities commonly used in automatic speech recognition and natural language processing. The MAP formulation offers a way to combine existing prior knowledge and a small set of newly acquired task-specific data

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Statistical and Discriminative Methods for Speech Recognition,erns or models for accurate pattern comparison. In this chapter, we discuss the issue of speech recognizer training from a broad perspective with root in the classical Bayes decision theory. We differentiate the method of classifier design by way of distribution estimation and the method of discrimi

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Voice Identification Using Nonparametric Density Matching,characterized by stable, speaker-unique probability density functions (PDFs). This chapter describes a method of comparing speech utterances to determine whether or not the underlying PDFs are the same, hence likely to have been spoken by the same person. The method is independent of assumptions abo

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