卷积增强变压器用于增强扬声器独立的失节性语音识别.
概括
本研究介绍了自动语音识别 (ASR) 的高级语音独立 (SI) 模型,以帮助患有脱节症的人. 新的模型显著提高了失节性说话者的语音识别准确性,解决了数据稀缺的挑战.
科学领域:
- 语音和语言处理 语言处理
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 发病症是一种影响口头沟通的运动语言障碍.
- 现有的自动语音识别 (ASR) 系统由于数据稀缺和扬声器变异性而与缺关节性语音作斗争.
- 扬声器独立 (SI) 方法对于开发ASR系统至关重要,这些系统可以帮助有沟通障碍的人.
研究的目的:
- 开发和评估自动语音识别 (ASR) 的扬声器独立 (SI) 模型,以适应脱节性言语.
- 通过转移学习和参数效率微调 (PEFT) 来应对失关节性ASR数据稀缺的挑战.
- 通过交叉数据集验证建立一个基准框架来评估SI模型的通用性.
主要方法:
- 开发基于Conformer的SI模型,使用具有选择性层结PEFT的三阶段转移学习管道.
- 预先训练了标准语音模型,并逐渐将其适应两个不同的缺关节病数据集 (TORGO和UA-Speech).
- 实施了交叉数据集验证策略,以评估在现实的交叉数据集场景中模型的概括性.
主要成果:
- 拟议的失关节性SI模型在孤立和连续语音识别任务中表现优于所有基线系统.
- 观察到显著的改进:TORGO数据集中单独语音的单词识别精度增加了21.9%,连续语音的单词错误率减少了18.5%.
- 在UA-Speech上,最佳SI模型显示比Whisper有14.6%的改进,比孤立语音识别的基本模型有28.3%.
结论:
- 开发的失关节性SI模型在识别失关节性言语方面表现出卓越的表现,为沟通辅助提供了一个有前途的解决方案.
- 交叉数据集验证显示,模型可以转录单独的单词,而不是严重的失声症的连续语音,这表明需要加强SI模型的概括.
- 需要进行进一步的研究,以提高SI模型的通用化能力,以实现持续性失关节性语音识别.
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