社区支持的共享基础设施,以支持语音可访问性
Mark Hasegawa-Johnson1, Xiuwen Zheng1, Heejin Kim1
1University of Illinois Urbana-Champaign.
Journal of speech, language, and hearing research : JSLHR
|September 26, 2024
概括
语音可访问性项目 (SAP) 为残疾人提供语音数据,以改进自动语音识别 (ASR) 技术. 这个资源加速了研究,旨在提高每个人的言语可访问性.
科学领域:
- 语音技术 语言技术
- 机器学习 机器学习
- 关于残疾人的研究.
背景情况:
- 语音可访问性项目 (SAP) 成立是为了解决机器学习中对多样化语音数据的需求.
- 语音障碍者经常面临当前自动语音识别 (ASR) 系统的挑战.
- 可访问的语音技术对于包容性和对数字工具的公平访问至关重要.
研究的目的:
- 介绍语音可访问性项目 (SAP) 作为ASR和机器学习研究人员的宝贵资源.
- 提供SAP首次发布数据包的基线分析.
- 促进针对言语障碍者改进的ASR系统的开发.
主要方法:
- 收集和策划的美国英语语音数据来自言语和/或语言障碍的个人.
- 参与者通过网络门户网站远程记录语音,根据需要使用个人设备和辅助技术.
- 语音数据是手动转录的,对诊断模式进行了注释的子集;参与者被分配到ASR实验的培训,开发和测试集中.
主要成果:
- 最初的数据包包括211名患有与帕金森病相关的脱节症的个体的言语.
- 基线ASR在患有脱节症的个体的测试语音中实现了36.3%的单词错误率,而典型发言者的错误率为3.4%.
- 对ASR模型的微调降低了词错误率为23.7%的失关节性语音数据.
结论:
- 一个大量的关节障碍和发声障碍的语音可以显著提升残疾人的语音技术.
- 预计SAP数据分发将加速可访问语音技术的研究和开发.
- 使用SAP数据的持续研究有可能创造更公平,更有效的ASR解决方案.
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