Journal ArticleDOI
Statistical-model-based speech enhancement systems
Yariv Ephraim
- Vol. 80, Iss: 10, pp 1526-1555
TLDR
A unified statistical approach for the three basic problems of speech enhancement is developed, using composite source models for the signal and noise and a fairly large set of distortion measures.Abstract:
Since the statistics of the speech signal as well as of the noise are not explicitly available, and the most perceptually meaningful distortion measure is not known, model-based approaches have recently been extensively studied and applied to the three basic problems of speech enhancement: signal estimation from a given sample function of noisy speech, signal coding when only noisy speech is available, and recognition of noisy speech signals in man-machine communication. Research on the model-based approach is integrated and put into perspective with other more traditional approaches for speech enhancement. A unified statistical approach for the three basic problems of speech enhancement is developed, using composite source models for the signal and noise and a fairly large set of distortion measures. >read more
Citations
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Proceedings ArticleDOI
SEGAN: Speech Enhancement Generative Adversarial Network
TL;DR: This work proposes the use of generative adversarial networks for speech enhancement, and operates at the waveform level, training the model end-to-end, and incorporate 28 speakers and 40 different noise conditions into the same model, such that model parameters are shared across them.
Journal ArticleDOI
A signal subspace approach for speech enhancement
Yariv Ephraim,H.L. Van Trees +1 more
TL;DR: The popular spectral subtraction speech enhancement approach is shown to be a signal subspace approach which is optimal in an asymptotic (large sample) linear minimum mean square error sense, assuming the signal and noise are stationary.
Journal ArticleDOI
Hidden Markov processes
Yariv Ephraim,Neri Merhav +1 more
TL;DR: An overview of statistical and information-theoretic aspects of hidden Markov processes (HMPs) is presented and consistency and asymptotic normality of the maximum-likelihood parameter estimator were proved under some mild conditions.
Journal ArticleDOI
Speech recognition in noisy environments: a survey
TL;DR: The survey indicates that the essential points in noisy speech recognition consist of incorporating time and frequency correlations, giving more importance to high SNR portions of speech in decision making, exploiting task-specific a priori knowledge both of speech and of noise, using class-dependent processing, and including auditory models in speech processing.
Book
Survey of the State of the Art in Human Language Technology
TL;DR: In this article, the authors present a glossary for language analysis and understanding in the context of spoken language input and output technologies, and evaluate their work with a set of annotated corpora.
References
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Journal ArticleDOI
A mathematical theory of communication
TL;DR: This final installment of the paper considers the case where the signals or the messages or both are continuously variable, in contrast with the discrete nature assumed until now.
Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Book
Table of Integrals, Series, and Products
TL;DR: Combinations involving trigonometric and hyperbolic functions and power 5 Indefinite Integrals of Special Functions 6 Definite Integral Integral Functions 7.Associated Legendre Functions 8 Special Functions 9 Hypergeometric Functions 10 Vector Field Theory 11 Algebraic Inequalities 12 Integral Inequality 13 Matrices and related results 14 Determinants 15 Norms 16 Ordinary differential equations 17 Fourier, Laplace, and Mellin Transforms 18 The z-transform
Journal ArticleDOI
A tutorial on hidden Markov models and selected applications in speech recognition
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.