抑制训练数据中的噪声差异以实现自动病理语音检测
Suppressing Noise Disparity in Training Data for Automatic Pathological Speech Detection
AI总结：
本文针对病理语音检测中训练数据噪声分布不一致导致模型学习噪声特征的问题，提出利用跨组噪声估计进行数据增强以统一噪声特性，实验证明该方法能有效引导模型关注病理判别线索。
摘要：
尽管自动病理语音检测方法在可获得干净录音时表现出良好的结果，但它们容易受到加性噪声的影响。最近的研究表明，常用于开发和评估此类方法的数据库存在噪声，且健康录音与病理录音之间的噪声特性不同。因此，在这些数据库上训练的自动方法往往学会区分噪声，而非语音病理。本文提出了一种缓解训练数据中这种噪声差异的方法。利用来自一组说话者录音的噪声估计来增强另一组说话者的录音，从而使所有录音的噪声特性保持一致。实验结果表明，该方法在缓解训练数据中的噪声差异方面是有效的，从而使自动病理语音检测能够专注于病理判别线索，而非噪声判别线索。
英文摘要：
Although automatic pathological speech detection approaches show promising results when clean recordings are available, they are vulnerable to additive noise. Recently it has been shown that databases commonly used to develop and evaluate such approaches are noisy, with the noise characteristics between healthy and pathological recordings being different. Consequently, automatic approaches trained on these databases often learn to discriminate noise rather than speech pathology. This paper introduces a method to mitigate this noise disparity in training data. Using noise estimates from recordings from one group of speakers to augment recordings from the other group, the noise characteristics become consistent across all recordings. Experimental results demonstrate the efficacy of this approach in mitigating noise disparity in training data, thereby enabling automatic pathological speech detection to focus on pathology-discriminant cues rather than noise-discriminant ones.
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