直接的发音观察揭示了自主监督语音模型的语音识别性能特征
Xuan Shi1, Tiantian Feng1, Kevin Huang1
1Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089 USA.
JASA express letters
|November 18, 2024
概括
语音模型的性能因说话者的背景而异,影响自动语音识别. 关节分析揭示了美国和印度英语发言者之间前音母生产的明显的形模式,突出了对包容性语音技术的需求.
科学领域:
- 语言学的语言学.
- 语音技术 语言技术
- 声学语音学的声音学
背景情况:
- 跨语言背景的发音变化会影响自动语音识别 (ASR).
- 自主监督的语音模型越来越多地被使用,但它们在不同口音的表现需要评估.
- 了解表达的差异对于改善ASR包容性至关重要.
研究的目的:
- 通过表达数据评估自主监督的语音模型的语音识别性能.
- 调查美国英语和印度英语使用者之间的发音差异.
- 分析观察到的语音识别变异的表达基础.
主要方法:
- 自主监督语音模型的语音识别评估.
- 实时磁共振成像 (MRI) 来捕捉关节运动.
- 在前声母产生过程中对状区域模式的比较分析.
主要成果:
- 在美国和印度英语说者之间观察到语音识别的显著差异.
- 这些差异在前音元音中尤为明显.
- 实时核磁共振扫描显示了每个说话组中与特定的前声母相关的明显的状区域模式.
结论:
- 关节变异,特别是在状区域,有助于自主监督模型中的语音识别差异.
- 当前的自主监督语音模型可能会由于语言背景而表现出偏见.
- 进一步研究模型变异和发音声学对于开发强大和包容性语音技术至关重要.
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