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Erschienen in:
Introduction Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

2. Gaussian Mixture Models

verfasst von : Dong Yu, Li Deng

Erschienen in: Automatic Speech Recognition

Verlag: Springer London

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Abstract

In this chapter we first introduce the basic concepts of random variables and the associated distributions. These concepts are then applied to Gaussian random variables and mixture-of-Gaussian random variables. Both scalar and vector-valued cases are discussed and the probability density functions for these random variables are given with their parameters specified. This introduction leads to the Gaussian mixture model (GMM) when the distribution of mixture-of-Gaussian random variables is used to fit the real-world data such as speech features. The GMM as a statistical model for Fourier-spectrum-based speech features plays an important role in acoustic modeling of conventional speech recognition systems. We discuss some key advantages of GMMs in acoustic modeling, among which is the easy way of using them to fit the data of a wide range of speech features using the EM algorithm. We describe the principle of maximum likelihood and the related EM algorithm for parameter estimation of the GMM in some detail as it is still a widely used method in speech recognition. We finally discuss a serious weakness of using GMMs in acoustic modeling for speech recognition, motivating new models and methods that form the bulk part of this book.

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Vorheriges Kapitel Introduction
Nächstes Kapitel Hidden Markov Models and the Variants
Fußnoten
1
Detailed derivation of these formulae can be found in [1], which we omit here. Related derivations for similar but more general models can be found in [2, 3, 6, 15, 18].
 
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Metadaten
Titel
Gaussian Mixture Models
verfasst von
Dong Yu
Li Deng
Copyright-Jahr
2015
Verlag
Springer London
Buch
Automatic Speech Recognition

Print ISBN: 978-1-4471-5778-6

Electronic ISBN: 978-1-4471-5779-3

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-1-4471-5779-3_2

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