An algorithm that improves speech intelligibility in noise for normal-hearing listeners
An algorithm that improves speech intelligibility in noise for normal-hearing listeners
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DOI：
10.1121/1.3184603
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发表时间：
2009-09-01
影响因子：
2.4
通讯作者：
Loizou, Philipos C.
中科院分区：
文献类型：
--
作者：
Kim, Gibak;Lu, Yang;Loizou, Philipos C.
传统的噪声抑制算法被证明可以改善语音质量，但不能提高语音的清晰度。基于对使用理想二进制掩码合成的语音的清晰度的研究，提出了一种算法，该算法将输入信号分解成时频(T-F)单元，并基于贝叶斯分类器做出关于每个T-F单元是由目标还是掩蔽者主导的二进制判决。用该算法合成了在低信噪比(-5和0分贝)下使用不同类型掩蔽器的语音，并将其提供给听力正常的听者进行识别。结果表明，与使用未经处理的刺激的听者相比，听者在可理解性方面有了显著的改善(在-5分贝的闲聊中超过60%)。这项研究的结果表明，能够可靠地估计每个T-F单元的SNR的算法可以提高语音的清晰度。(C)2009年美国声学学会。[DOI：10.1121/1.3184603]
Traditional noise-suppression algorithms have been shown to improve speech quality, but not speech intelligibility. Motivated by prior intelligibility studies of speech synthesized using the ideal binary mask, an algorithm is proposed that decomposes the input signal into time-frequency (T-F) units and makes binary decisions, based on a Bayesian classifier, as to whether each T-F unit is dominated by the target or the masker. Speech corrupted at low signal-to-noise ratio (SNR) levels (-5 and 0 dB) using different types of maskers is synthesized by this algorithm and presented to normal-hearing listeners for identification. Results indicated substantial improvements in intelligibility (over 60% points in -5 dB babble) over that attained by human listeners with unprocessed stimuli. The findings from this study suggest that algorithms that can estimate reliably the SNR in each T-F unit can improve speech intelligibility. (C) 2009 Acoustical Society of America. [DOI: 10.1121/1.3184603]