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Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

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Published on: August 9, 2024

自动语音听测:它可以使用开源预训练的Kaldi-NL自动语音识别工作吗?

Gloria Araiza-Illan^1,2, Luke Meyer^1,2, Khiet P Truong³

¹Department of Otorhinolaryngology, Head and Neck Surgery, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.

Trends in hearing

|March 14, 2024

概括

这项研究引入了使用Kaldi-NL.NL的自动化数字噪声 (DIN) 听力选测试. 该自动化系统准确评估口语反应,显示在听力评估中临床使用的潜力.

关键词:

自动语音识别自动语音识别数字在噪音中的测试语音听力测量语音听力测量语音感知语音感知语音噪音听力测试语音噪音听力测试

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科学领域:

听力学听力学是指听力学.
语音处理语音处理
计算语言学计算语言学

背景情况:

数字在噪声 (DIN) 测试是各种人群听力查的一个有价值的工具.
目前的DIN测试管理依赖于人类监督员或手动响应输入.
自动化DIN测试可以提高听力评估的效率和可访问性.

研究的目的:

开发和评估一个自动化数字在噪声 (DIN) 测试系统,使用Kaldi-NL工具包进行口语响应评估.
评估Kaldi-NL系统在噪声中准确转录口语数字方面的性能.
为了确定自动转录错误对语音接收值 (SRT) 输出的影响.

主要方法:

使用开源的Kaldi-NL自动语音识别工具包开发了一个自动化的DIN测试.
30名自称听力正常的荷兰成年人参与了这项研究.
该系统评估了口语响应,并通过文字错误率 (WER) 和通过启动模拟对SRT的影响来衡量其性能.

主要成果:

卡尔迪-NL系统的平均词错误率 (WER) 在参与者中为5.0%.
每位参与者平均有三个三胞胎包含解码错误.
模拟表明,多达四个有解码错误的三胞胎对语音接收值 (SRT) 的影响最小,保持在典型的变化范围内.

结论:

建议使用Kaldi-NL的自动化DIN测试设置可用于临床应用.
该系统对未经监督的听力查和评估充满希望.
进一步验证可能会证实它在现实世界听力学环境中的实用性.