用噪声估计对齐健康与病理性语音的噪声差异，提升病理语音检测准确率。
Suppressing Noise Disparity in Training Data for Automatic Pathological Speech Detection
- 用一组语音的噪声估计去增强另一组语音，使噪声特性一致。
- 实验表明该方法显著降低训练数据中的噪声差异，提升检测性能。
- 适合研究语音病理检测、数据噪声问题或医疗语音分析的学者。
尽管在干净录音下自动病理语音检测方法表现良好，但其对加性噪声敏感。最近研究表明，常用数据库中健康与病理性语音的噪声特性存在差异。因此，基于此类数据训练的模型常学习区分噪声而非病理特征。本文提出一种方法，利用一组说话人录音的噪声估计来增强另一组录音，使所有录音的噪声特性趋于一致。实验结果证明该方法能有效缓解训练数据中的噪声差异，使自动病理语音检测聚焦于病理判别线索，而非噪声判别线索。
原文摘要 · Abstract (English)
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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