文章基本信息

标题：ADAPTIVE SPECTRAL SUBTRACTION FOR ROBUST SPEECH RECOGNITION
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作者：JUNG-SEOK YOON ; JI-HWAN KIM ; JEONG-SIK PARK 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2018
卷号：96
期号：4
页码：1018
出版社：Journal of Theoretical and Applied
摘要：Speech recognition rate degrades drastically in extreme noisy environments. Spectral subtraction is one of the representative noise reduction method, but it is vulnerable to non-stationary noise although it is quite effective for stationary noise. In this paper, we propose an adaptive spectral subtraction method to improve the speech recognition performance. The proposed method is to consistently update the noise component in non-speech regions and remove the corresponding component in following speech regions. To validate of the noise reduction performance, we conducted several experiments for each noise power level. Our approach achieved better performance compared to the conventional spectral subtraction approach.
关键词：Noise Reduction; Spectral Subtraction; Speech Recognition; Voice Activity Detection