通过模拟和合成增强来改善失节性语音细分
Saeid Alavi Naeini1,2, Leif Simmatis1, Deniz Jafari1,2
1KITE, Toronto Rehabilitation Institute, University Health Network (UHN) Toronto ON M5G 2A2 Canada.
概括
通过模拟和合成变体来增强语音数据,可以显著改善用于神经疾病诊断的自动语音识别. 这减少了在语音细分任务中需要大量临床数据的需求.
科学领域:
- 语音处理 语音处理
- 神经系统疾病的诊断神经系统疾病的诊断
- 机器学习是机器学习.
背景情况:
- 语音助听器的声学特征用于诊断神经疾病和监测症状.
- 将音频信号的时间细分成单词对于特征提取至关重要.
- 自动语音识别 (ASR) 和序列对齐可以自动化语音细分.
研究的目的:
- 探索增强方法,以提高ASR表现的失节性言语.
- 评估使用模拟和合成数据用于ASR模型微调的可行性.
- 减少对稀缺和隐私敏感的临床数据的依赖.
主要方法:
- 微调预先训练的ASR模型,使用两个增强策略: 1) 健康的扬声器改变速度/大声度, 2) 合成语音与不同的速度/口音.
- 评估模型在失关节性发言数据上的表现.
- 将增强方法与微调方法与真实临床数据进行比较.
主要成果:
- 使用模拟和合成数据的增强超越了仅在真实临床数据上微调的模型.
- 性能匹配模型是根据真实数据与合成语音相结合进行微调的.
- 最好的模型实现了5.7%的文字错误率和94.4%的神经疾病数据的准确性.
- 单词细分与基本真相相比的平均交叉比合并率为89.2%.
结论:
- 模拟和合成数据增强有效地减少了对ASR模型精细调整失节性语音的真实临床数据的需求.
- 这些方法提高了神经疾病评估的语音细分精度.
- 该方法为临床语音分析提供了一个可扩展和保护隐私的解决方案.
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