Abstract
Automatic speech recognition systems based on deep learning are mainly trained under empirical risk minimization (ERM). Since ERM utilizes the averaged performance on the data samples regardless of a group such as healthy or dysarthric speakers, ASR systems are unaware of the performance disparities across the groups. This results in biased ASR systems whose performance differences among groups are severe. In this study, we aim to improve the ASR system in terms of group robustness for dysarthric speakers. To achieve our goal, we present a novel approach, sample reweighting with sample affinity test (Re-SAT). Re-SAT systematically measures the debiasing helpfulness of the given data sample and then mitigates the bias by debiasing helpfulness-based sample reweighting. Experimental results demonstrate that Re-SAT contributes to improved ASR performance on dysarthric speech without performance degradation on healthy speech.
Abstract (translated)
基于深度学习的自动语音识别系统主要基于经验风险最小化(ERM)进行训练。由于ERM无论使用数据样本中的平均表现，都会使用健康或发音困难的听众的表现，因此语音识别系统不知道不同群组之间的性能差异。这导致具有偏见的语音识别系统，其不同群组的性能差异非常严重。在本研究中，我们旨在改善发音困难的听众群组的鲁棒性，以提高发音困难的语音自动语音识别系统的性能。为了实现我们的目标，我们提出了一种新方法，即样本亲和力测试(Re-SAT)。Re-SAT systematically measures给定数据样本的去偏帮助性，然后通过去偏帮助性的样本重新加权来缓解偏见。实验结果显示，Re-SAT有助于改善发音困难的语音自动语音识别系统的性能，而健康语音的性能并未受到负面影响。
URL
https://arxiv.org/abs/2305.13108
