一项用于自动语音识别的概念验证研究,用于从高科技 AAC 系统中转录 AAC 扬声器的语音
Szu-Han Kay Chen1, Conner Saeli2, Gang Hu2
1Department of Communication Sciences and Disorders, The University of New Hampshire, Durham, New Hampshire, USA.
Assistive technology : the official journal of RESNA
|September 25, 2023
概括
一个新的增强和替代通信-自动语音识别 (AAC-ASR) 模型准确地转录来自高科技 AAC 系统的语音. 这项技术有助于对使用AAC设备的个人进行语言样本分析.
科学领域:
- 语音和语言病理学 语言病理学
- 计算机科学 计算机科学
- 辅助技术 辅助技术 辅助技术
背景情况:
- 自动语音识别 (ASR) 用于非典型的语音和语言样本的转录.
- 在高科技增强型和替代型通信 (AAC) 系统中使用ASR仍然未被探索.
- 语言样本分析在沟通科学和障碍学中至关重要.
研究的目的:
- 研究使用AAC-ASR模型来转录来自高科技AAC系统的语言样本的可行性.
- 将开发的AAC-ASR模型的转录精度与已建立的ASR模型 (CMU Sphinx,谷歌语音到文本) 进行比较.
主要方法:
- 开发了一个概念验证的AAC-ASR模型来转录模拟的AAC扬声器语言样本.
- 使用测试文件评估了AAC-ASR模型的Word错误率 (WER).
- 将AAC-ASR模型的WER与CMU Sphinx和谷歌语音到文本进行了比较.
主要成果:
- 开发的 AAC-ASR 模型实现了 28.6% 的 Word 错误率 (WER).
- 这在测试的AAC语音样本上显著超过了CMU Sphinx (70.7% WER) 和谷歌语音到文本 (86.2% WER).
- 证明了对高科技 AAC 模拟语言样本的自动转录的可行性.
结论:
- 在AAC-ASR模型显示承诺自动转录高科技AAC生成的语言样本.
- 这项技术可以支持通信科学中的语言样本分析.
- 未来的研究应该专注于各种AAC语音数据集和个人用户语音模式的完善.
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