脑卒中后语音转录 (PSST) 挑战
Robert C Gale1, Mikala Fleegle2, Gerasimos Fergadiotis2
1Oregon Health and Science University, Portland, Oregon, USA.
LREC ... International Conference on Language Resources & Evaluation : [proceedings]. International Conference on Language Resources & Evaluation
|January 13, 2025
概括
脑卒中后语音转录挑战为阿法西亚进行了先进的自动语音识别,提高了语音转录的准确性. 改进的转录提升了中风幸存者的下游词语发音评估.
科学领域:
- 计算语言学计算语言学
- 语音处理 语音处理
- 语言的神经科学语言的神经科学.
背景情况:
- 脑卒中后的一种语言障碍 - - 失言症 (Aphasia) 显著影响了语音的产生.
- 准确的语音转录对于评估和康复失语至关重要.
- 现有的语音识别技术经常与失调的语音模式作斗争.
研究的目的:
- 引入中风后语音转录 (PSST) 挑战及其相关数据集.
- 评估自动语音识别 (ASR) 系统对脑卒中后语音的音声转录的性能.
- 在下游任务中评估改进的语音转录的实用性,以确定正确的单词发音.
主要方法:
- 来自AphasiaBank的新型数据集的创建,包括用于对抗命名任务的音频录音和语音转录.
- 任务A:使用Phoneme错误率 (PER) 和特征错误率 (FER) 开发和评估语音转录的ASR模型.
- 任务B:评估分类算法,以确定目标词的正确发音,使用基线和增强的成绩单.
主要成果:
- 领先的ASR模型实现了9.9%的FER和20.0%的PER,比基线显著改善.
- 挑战者在直接发音评估任务 (任务B) 中的表现并没有超过基线算法.
- 将任务A的改进成绩单集成到任务B算法中,获得了92.8%的准确率和0.921 F1分数.
结论:
- PSST挑战数据集和任务有助于在语音障碍的ASR方面取得进展.
- 改进的语音转录显著提高了下游语音评估任务的准确性.
- 进一步的研究可以利用这些发现,为治疗失语症提供更好的临床工具.
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