音节率调整调制 (SRAM) 预测了清晰和对话式的语音可理解性
Ye Yang1, Fan-Gang Zeng1,2
1Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States.
Frontiers in human neuroscience
|February 27, 2024
概括
一个新的音节率调整调制 (SRAM) 指数准确地预测了清晰和对话式语音的语音可理解性. SRAM的性能优于现有的指标和语音识别系统,强调了音节速率在可理解性预测中的重要性.
科学领域:
- 语音处理 语音处理
- 声学语音学的声音学.
- 信号处理 信号处理
背景情况:
- 对话可理解性的客观预测对于电信和人机交互至关重要.
- 使用信号噪声比率 (SNR) 的传统方法难以解释清晰言语的增强可理解性.
- 没有现有的客观指标可靠地预测句子层面的清晰言语益处.
研究的目的:
- 提出和验证一种新的客观度量,即音节率调整调制 (SRAM) 指数,用于预测语音可理解性.
- 评估SRAM的性能与既定的可理解度量和最先进的自动语音识别系统相比.
- 为了证明音节速率在可理解性预测中的重要性.
主要方法:
- 开发了音节率调整调制 (SRAM) 指数,该指数估计来自短语段 (短短1秒) 的音节率以上的调制功率.
- 将SRAM与三个参考指标进行比较:基于信封回归的语音传输指数 (ER-STI),助听器语音感知指数版本2 (HASPI-v2) 和短期客观可理解性 (STOI).
- 对5个自动语音识别系统进行了SRAM评估:亚马逊转录,微软Azure语音到文本,谷歌语音到文本,wav2vec2和Whisper.
主要成果:
- 拟议的SRAM指数在预测语音可理解性方面显著超过了ER-STI,HASPI-v2和STOI.
- 与所有五个评估的自动语音识别系统相比,SRAM表现出更高的性能.
- 与总调制功率 (TMP) 的比较证实了音节速调整在SRAM有效性中的关键作用.
结论:
- SRAM指数提供了一个有希望的客观指标,用于预测清晰和对话式语音的可理解性.
- 萨拉姆能够捕捉清晰语音的好处,这对理解语音的产生和感知有影响.
- 潜在的应用包括对言语材料进行高可理解性选,以及开发将对话式言语转换为清晰言语的方法.
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