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STATISTICAL-MODEL-BASED SPEECH ENHANCEMENT SYSTEMS
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DOI：10.1109/5.168664.png)
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摘要
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Speech enhancement has been a challenge for many researchers for almost three decades. The problem involves improving the performance of speech communication systems in noisy environments. 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. These problems comprise 1) signal estimation from a given sample function of noisy speech, 2) signal coding when only noisy speech is available, and 3) recognition of noisy speech signals in man-machine communication. In this paper, the recent 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.
Keyword:
SPECTRAL AMPLITUDE ESTIMATOR
NOISY SPEECH
VECTOR QUANTIZATION
MAXIMUM-LIKELIHOOD
EM ALGORITHM
PROBABILISTIC FUNCTIONS
DISTORTION MEASURES
DISTANCE MEASURES
WHITE NOISE
RECOGNITION
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期刊
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25.9
论文数:
9.9K
被引数:
4.5W
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PROCEEDINGS OF THE IEEEIF25.9
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