在初级渐进性失言症中自动检测基于关节的失语
Jiachen Lian1, Xuanru Zhou1, Chenxu Guo1
1Jiachen Lian, and Gopala Krishna Anumanchipalli are with the University of California, Berkeley; Xuanru Zhou, Chenxu Guo, and Zongli Ye are with Zhejiang University and contributed equally as co-second authors; Zoe Ezzes, Jet M.J. Vonk, Brittany Morin, David Baquirin, Zachary Miller, and Maria Luisa Gorno-Tempini are with the University of California, San Francisco.
IEEE journal of selected topics in signal processing
|January 22, 2026
概括
这项研究引入了SSDM 2.0,这是一种用于检测语音不流的先进系统,显著改善了对像初级渐进性失言症这样的疾病的病态语音转录.
科学领域:
- 计算语言学计算语言学
- 语音处理 语音处理
- 临床语言学 临床语言学
背景情况:
- 语音体对于语言分析至关重要,包括流利性和错误检测.
- 失流性检测对于诊断诸如初级渐进性失言症 (PPA) 等疾病至关重要.
- 现有的系统在捕捉语音不流的全谱方面存在局限性.
研究的目的:
- 开发一个改进的系统 (SSDM 2.0),用于检测和转录语音失语.
- 为了加强对非流利变体初级渐进性失言症 (nfvPPA) 发言的关节性失言症的分析.
- 为临床应用创造一个更具可扩展性和准确性的工具.
主要方法:
- 提出了一种新的神经关节流动,用于不流意识的语音表示.
- 开发了一种全连接式子序列对齐器,用于全面的失流性类型捕获.
- 整合错误发音提示和一致性学习到大型语言模型 (LLM) 中,用于上下文失语学习.
- 策划和开源的Libri-Co-Dys,最大的共同不流的库.
- 介绍了SSDM-L,一个模块化,轻量级的模型用于临床部署.
主要成果:
- 在失流性检测和转录方面,SSDM 2.0显著优于之前的模型.
- 该系统在 nfvPPA 体上表现出高效,其特点是关节流失.
- Libri-Co-Dys为共同失语研究提供了前所未有的资源.
结论:
- SSDM 2.0 代表了语言不流性分析的实质性进步.
- 该系统对改善病态语音的诊断和转录充满希望.
- 开源的库和轻量级模型促进了更广泛的研究和临床采用.
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