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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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期刊全称Automatic Speech and Speaker Recognition
期刊简称Advanced Topics
影响因子2023Chin-Hui Lee,Frank K. Soong,Kuldip K. Paliwal
视频video
学科分类The Springer International Series in Engineering and Computer Science
图书封面Titlebook: Automatic Speech and Speaker Recognition; Advanced Topics Chin-Hui Lee,Frank K. Soong,Kuldip K. Paliwal Book 1996 Kluwer Academic Publisher
影响因子Research in the field of automatic speech and speakerrecognition has made a number of significant advances in the last twodecades, influenced by advances in signal processing, algorithms,architectures, and hardware. These advances include: the adoption of astatistical pattern recognition paradigm; the use of the hidden Markovmodeling framework to characterize both the spectral and the temporalvariations in the speech signal; the use of a large set of speechutterance examples from a large population of speakers to train thehidden Markov models of some fundamental speech units; theorganization of speech and language knowledge sources into astructural finite state network; and the use of dynamic, programmingbased heuristic search methods to find the best word sequence in thelexical network corresponding to the spoken utterance. ..Automatic Speech and Speaker Recognition: Advanced Topics. groupstogether in a single volume a number of important topics on speech andspeaker recognition, topics which are of fundamental importance, butnot yet covered in detail in existing textbooks. Although no explicitpartition is given, the book is divided into five parts: Chapters1-2 are devoted to techn
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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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