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首页> 外文期刊>Computer speech and language >Intelligibility enhancement of HMM-generated speech in additive noise by modifying Mel cepstral coefficients to increase the glimpse proportion
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Intelligibility enhancement of HMM-generated speech in additive noise by modifying Mel cepstral coefficients to increase the glimpse proportion

机译：通过修改Mel倒谱系数以增加瞥见比例来增强HMM生成的语音在可加性噪声中的清晰度

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摘要

This paper describes speech intelligibility enhancement for Hidden Markov Model (HMM) generated synthetic speech in noise. We present a method for modifying the Mel cepstral coefficients generated by statistical parametric models that have been trained on plain speech. We update these coefficients such that the glimpse proportion - an objective measure of the intelligibility of speech in noise - increases, while keeping the speech energy fixed. An acoustic analysis reveals that the modified speech is boosted in the region 1-4kHz, particularly for vowels, nasals and approximants. Results from listening tests employing speech-shaped noise show that the modified speech is as intelligible as a synthetic voice trained on plain speech whose duration, Mel cepstral coefficients and excitation signal parameters have been adapted to Lombard speech from the same speaker. Our proposed method does not require these additional recordings of Lombard speech. In the presence of a competing talker, both modification and adaptation of spectral coefficients give more modest gains.
机译：本文介绍了在语音中针对隐马尔可夫模型（HMM）生成的合成语音的语音清晰度增强。我们提出了一种方法，用于修改由已经在普通语音上训练的统计参数模型生成的Mel倒谱系数。我们更新这些系数，以便在保持语音能量固定的前提下，瞥见比例（即语音在语音中的清晰度）的客观衡量指标得以提高。声学分析表明，修改后的语音会在1-4kHz的范围内增强，特别是对于元音，鼻音和近似音而言。使用语音形噪声的听力测试结果表明，修改后的语音与在普通语音上训练的合成语音一样可理解，其持续时间，梅尔倒谱系数和激励信号参数已适应来自同一说话者的伦巴德语音。我们提出的方法不需要这些额外的伦巴底语语音记录。在有竞争性发言者的情况下，频谱系数的修改和自适应都可提供更为适度的增益。

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