跨性别音素尺度下的多通道语音增强算法评估
Evaluating Multichannel Speech Enhancement Algorithms at the Phoneme Scale Across Genders
AI总结：
本文在音素尺度上评估多通道语音增强算法，发现性别差异在语句级微小但在音素级显著，算法对女性语音（尤其塞音、擦音、元音）干扰抑制更好且伪影更少。
摘要：
多通道语音增强算法对于提高嘈杂环境中语音信号的可懂度至关重要。这些算法通常在语句级别进行评估，但这种方法忽视了不同音素类别以及男性和女性说话者之间所观察到的声学特征的差异。在本文中，我们研究了性别和语音内容对语音增强算法的影响。我们通过概述音素和性别特定的频谱特征来论证这一方法的合理性。我们的实验表明，虽然语句级别上性别之间的差异很小，但在音素级别上出现了显著的差异。结果显示，所测试的算法能更好地减少干扰，并在女性语音上产生更少的伪影，尤其是在塞音、擦音和元音中。此外，在感知和语音识别指标方面，它们对女性语音表现出更好的性能。
英文摘要：
Multichannel speech enhancement algorithms are essential for improving the intelligibility of speech signals in noisy environments. These algorithms are usually evaluated at the utterance level, but this approach overlooks the disparities in acoustic characteristics that are observed in different phoneme categories and between male and female speakers. In this paper, we investigate the impact of gender and phonetic content on speech enhancement algorithms. We motivate this approach by outlining phoneme- and gender-specific spectral features. Our experiments reveal that while utterance-level differences between genders are minimal, significant variations emerge at the phoneme level. Results show that the tested algorithms better reduce interference with fewer artifacts on female speech, particularly in plosives, fricatives, and vowels. Additionally, they demonstrate greater performance for female speech in terms of perceptual and speech recognition metrics.
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