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计算机系统应用:2018,27(12):187-191
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低信噪比下高可懂度语音增强算法
刘鹏
(山西工程技术学院 信息工程与自动化系, 阳泉 045000)
High Intelligibility Speech-Enhancement Algorithm Under Low SNR Condition
LIU Peng
(Department of Information Engineering and Automation, Shanxi Institute of Technology, Yangquan 045000, China)
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投稿时间:2018-04-23    修订日期:2018-05-14
中文摘要: 提出了低信噪比下高可懂度的基于分段信噪比相对均方根(RMS)的语音增强子空间算法.现有的多数语音增强算法在低信噪比的恶劣条件下,改善带噪语音质量的同时通常会伴有语音可懂度的降低.一个重要原因是这些算法大都仅基于最小均方误差(MMSE)来抑制语音失真,却忽略了语音增强算法所导致的语音失真对差异类型语音分段的可懂度影响程度不同.为了改进这一缺点,提出了基于短时信噪比RMS对语音分段进行分类,然后调整处于信噪比中均方根语音分段的增益矩阵分量,来减小语音失真对增强语音可懂度的影响.客观评价实验说明,改进算法可以改善增强语音可懂度归一化协方差评价法(NCM)的评测值.主观试听实验说明,改进算法的确提升了增强后语音的可懂度.
Abstract:A higher intelligibility subspace speech-enhancement algorithm based on the relative Root Mean Square (RMS) of speech segmental Signal-to-Noise Ratio (SNR) with low SNR is proposed. Under harsh conditions of low SNR, an improvement of noisy speech quality based on the majority existing speech-enhancement algorithms is often accompanied by a decrease in speech intelligibility. One important reason is that these algorithms only use Minimum Mean Square Error (MMSE) to constrain speech distortions but ignore that speech distortions caused by speech enhancement algorithms have different intelligibility influences on different speech segments. In order to overcome this disadvantage, the RMS of short-time segmental SNR was used to classify speech segments. Then the gain matrix components of middle-level RMS segments were modified to reduce the influence of speech distortion on enhanced speech intelligibility. Objective evaluation shows that the improved algorithm can improve enhanced speech intelligibility Normalized Covariance Metric (NCM) evaluation values. Subjective audition shows that the proposed algorithm does improve the enhanced speech intelligibility.
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刘鹏.低信噪比下高可懂度语音增强算法.计算机系统应用,2018,27(12):187-191
LIU Peng.High Intelligibility Speech-Enhancement Algorithm Under Low SNR Condition.COMPUTER SYSTEMS APPLICATIONS,2018,27(12):187-191

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