超越全球指标:对可解释语音障碍检测系统的公平性分析
Mariel Estevez1, Cyntia Bonomi2, Dayana Ribas3
1Instituto de Investigación en Ciencias de la Computación (ICC), UBA-CONICET, Pabellón Cero+Infinito - Ciudad Universitaria, Buenos Aires C1428EGA, Argentina.
Journal of voice : official journal of the Voice Foundation
|December 20, 2025
概括
自动语音障碍检测 (AVDD) 系统显示人口偏见,错误分类年长的健康个体和年轻的失调个体. 集团特定校准通过解决这些绩效差异来提高可靠性.
科学领域:
- 生物医学工程 生物医学工程
- 语音科学 语言科学
- 医疗保健中的人工智能
背景情况:
- 自动语音障碍检测 (AVDD) 系统对于诊断语音障碍至关重要.
- 现有的AVDD系统在不同的人口群体中经常表现出性能差异.
- 全球性能指标可能会掩盖AVDD系统准确性的潜在偏差.
研究的目的:
- 在AVDD系统中调查和量化人口依赖偏差.
- 评估特定组校准策略的有效性,以提高AVDD可靠性.
- 识别导致跨年龄和性别队伍绩效差异的因素.
主要方法:
- 使用语音障碍数据集与人口统计元数据分析AVDD系统.
- 通过使用规范化成本和交叉来评估基于性别和年龄的群体的系统性能.
- 应用特定集团的校准技术,以减轻校准错误.
主要成果:
- 在人口群体之间观察到显著的绩效差异,尽管整体指标强.
- AVDD系统表现出偏见,错误地分类了年长的健康说话者和年轻的失调说话者.
- 特定组的校准改进了后置概率估计,并减少了过度自信.
结论:
- 全球性能指标不足以对AVDD系统进行公平评估.
- 人口特异性分析和校准对于可靠的语音障碍检测至关重要.
- 这些发现为改善生物医学分类任务中的偏差缓解提供了一个框架.
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