探索实用指标,以支持自动语音识别评估
E A Draffan1, Mike Wald1, Chaohai Ding1
1ECS, University of Southampton, UK.
Studies in health technology and informatics
|August 28, 2023
概括
单单单词错误率不足以评估自动语音识别的质量. 分析平行语言特征的新指标提高了学术环境中的转录准确性和包容性.
科学领域:
- 语音技术 语言技术
- 人与计算机的交互
- 机器学习 机器学习
背景情况:
- 传统的自动语音识别 (ASR) 的文字错误率 (WER) 度量不能充分捕捉错误类型或原因,阻碍了开发人员的反.
- 在学术环境中,转录和标题中的ASR错误会对理解产生负面影响,特别是对于残疾学生来说,由于特定领域的语言和非母语使用者.
- 现有的ASR与诸如杂的环境和学术讲座中常见的专用术语等挑战作斗争.
研究的目的:
- 讨论在评估ASR输出质量时,除了文字错误率之外,使用额外的指标.
- 探索这些指标如何为ASR中的机器学习过程提供更好的反.
- 通过改进ASR,促进虚拟会议系统中的更具包容性的实践.
主要方法:
- 检查文本错误率在评估ASR准确性的局限性.
- 在ASR评估中研究 paralinguistic特征 (时机,语调,语音质量,语音理解) 的结合.
- 分析反机制,以增强ASR系统的机器学习过程.
主要成果:
- 文字错误率为ASR错误的性质和原因提供了有限的洞察力.
- 同语言特征为学术ASR提供了更丰富的语言细微差别理解,这对学术ASR至关重要.
- 整合新的指标可以导致更准确和更具上下文意识的ASR输出.
结论:
- 增强的ASR评估需要超越简单单词准确性的指标.
- 结合并语分析可以提高ASR在复杂的学术环境中的表现.
- 先进的ASR评估促进了虚拟通信平台更大的包容性和可访问性.
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