Effect of Noise Reduction Gain Errors on Simulated Cochlear Implant Speech Intelligibility
Effect of Noise Reduction Gain Errors on Simulated Cochlear Implant Speech Intelligibility
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DOI：
10.1177/2331216519825930
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发表时间：
2019-02-13
影响因子：
2.7
通讯作者：
Dau, Torsten
中科院分区：
文献类型：
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作者：
Kressner, Abigail A.;May, Tobias;Dau, Torsten
已经提出，对于耳蜗植入（CI）接受者在噪声中获得高语音可懂度的最重要因素是通过例如最小化语音段之间的间隙中的噪声量来保持语音在时间和频率上的低频幅度调制。相反，也有人认为，语音信号的瞬态部分，如语音起始，为语音清晰度提供了最重要的信息。本研究调查了这两个因素的相对影响，CI收件人的降噪的潜在好处，系统地引入语音段，语音间隙，和它们之间的过渡噪声估计误差。这些噪声估计误差的引入直接引起这些区域中的每一个内的噪声降低增益中的误差。语音清晰度在固定和调制噪声，然后测量使用CI模拟测试正常听力的听众。结果表明，最大限度地减少语音间隙中的噪声可以提高可懂度，至少在调制噪声。然而，当间隙中的噪声被最小化并且语音瞬变被保留时，获得了显著更大的改进。这些结果意味着识别语音段和语音间隙之间的边界的能力可能是降噪算法的最重要因素之一，因为知道边界使得有可能最小化间隙中的噪声以及增强语音的低频幅度调制。
It has been suggested that the most important factor for obtaining high speech intelligibility in noise with cochlear implant (CI) recipients is to preserve the low-frequency amplitude modulations of speech across time and frequency by, for example, minimizing the amount of noise in the gaps between speech segments. In contrast, it has also been argued that the transient parts of the speech signal, such as speech onsets, provide the most important information for speech intelligibility. The present study investigated the relative impact of these two factors on the potential benefit of noise reduction for CI recipients by systematically introducing noise estimation errors within speech segments, speech gaps, and the transitions between them. The introduction of these noise estimation errors directly induces errors in the noise reduction gains within each of these regions. Speech intelligibility in both stationary and modulated noise was then measured using a CI simulation tested on normal-hearing listeners. The results suggest that minimizing noise in the speech gaps can improve intelligibility, at least in modulated noise. However, significantly larger improvements were obtained when both the noise in the gaps was minimized and the speech transients were preserved. These results imply that the ability to identify the boundaries between speech segments and speech gaps may be one of the most important factors for a noise reduction algorithm because knowing the boundaries makes it possible to minimize the noise in the gaps as well as enhance the low-frequency amplitude modulations of the speech.