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Speech energy redistribution for intelligibility improvement in noise based on a perceptual distortion measure

机译：基于感知失真测度的语音能量重新分配，以提高噪声的清晰度

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摘要

A speech pre-processing algorithm is presented that improves the speech intelligibility in noise for the near-end listener. The algorithm improves intelligibility by optimally redistributing the speech energy over time and frequency according to a perceptual distortion measure, which is based on a spectro-temporal auditory model. Since this auditory model takes into account short-time information, transients will receive more amplification than stationary vowels, which has been shown to be beneficial for intelligibility of speech in noise. The proposed method is compared to unprocessed speech and two reference methods using an intelligibility listening test. Results show that the proposed method leads to significant intelligibility gains while still preserving quality. Although one of the methods used as a reference obtained higher intelligibility gains, this happened at the cost of decreased quality. Matlab code is provided.
机译：提出了一种语音预处理算法，可提高近端听众在噪声中的语音清晰度。该算法通过根据基于时空听觉模型的感知失真度量在时间和频率上最佳地重新分配语音能量来提高清晰度。由于此听觉模型考虑了短时信息，因此瞬态将比固定元音获得更多的放大，这已被证明对噪声中语音的可理解性是有益的。使用可听度测试，将该方法与未处理语音和两种参考方法进行了比较。结果表明，所提出的方法在保持质量的同时，还可以明显提高清晰度。尽管作为参考的一种方法获得了更高的清晰度，但这是以降低质量为代价的。提供了Matlab代码。

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