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http://dx.doi.org/10.5909/JBE.2018.23.4.511

Adaptation of Classification Model for Improving Speech Intelligibility in Noise  

Jung, Junyoung (School of Electrical Engineering, Soongsil University)
Kim, Gibak (School of Electrical Engineering, Soongsil University)
Publication Information
Journal of Broadcast Engineering / v.23, no.4, 2018 , pp. 511-518 More about this Journal
Abstract
This paper deals with improving speech intelligibility by applying binary mask to time-frequency units of speech in noise. The binary mask is set to "0" or "1" according to whether speech is dominant or noise is dominant by comparing signal-to-noise ratio with pre-defined threshold. Bayesian classifier trained with Gaussian mixture model is used to estimate the binary mask of each time-frequency signal. The binary mask based noise suppressor improves speech intelligibility only in noise condition which is included in the training data. In this paper, speaker adaptation techniques for speech recognition are applied to adapt the Gaussian mixture model to a new noise environment. Experiments with noise-corrupted speech are conducted to demonstrate the improvement of speech intelligibility by employing adaption techniques in a new noise environment.
Keywords
speech intelligibility; noise suppression; binary mask; Gaussian mixture model; adaptation;
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Times Cited By KSCI : 2  (Citation Analysis)
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