通过LATTE:一种低延迟的声学建模方法来实现实时通信的语音到文本识别
Qurat Ul Ain1, Hammad Afzal1,2, Fazli Subhan3
1National University of Sciences & Technology (NUST), Islamabad, Pakistan.
Big data
|February 9, 2026
概括
这项研究介绍了LATTE,这是一种用于实时脱节性关节炎语音识别的新框架. 它显著提高了语音障碍者的转录精度,提高了沟通的可访问性.
科学领域:
- 语音和听力科学 语言和听力科学
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 失节症是一种运动发言障碍,严重影响了沟通.
- 传统的自动语音识别 (ASR) 系统在与失节性语音作斗争,特别是在严重的情况下.
- 需要针对语音障碍量身定制的先进的ASR解决方案.
研究的目的:
- 开发和评估一个低延迟的框架,用于实时的失关节性语音识别.
- 为了提高ASR的准确性和可访问性,对于患有脱节症的人来说.
- 为数字应用程序和交互式环境提供坚实的基础,以处理语音障碍.
主要方法:
- 引入低延迟声转录和文本编码 (LATTE) 框架.
- 将预处理,声处理和转录映射集成到一个统一的管道中.
- 使用混合架构,将卷积和双向时间层结合起来,用于特征提取和时间依赖模型.
主要成果:
- 在UA-Speech数据集上,LATTE实现了12.5%的单词错误率 (WER).
- 语音错误率 (PER) 记录在8.3%,字符错误率 (CER) 在1%.
- 证明了精确的,低延迟的发言障碍发言的转录.
结论:
- 在实时ASR中,LATTE为失节性言语提供了显著的进步.
- 该框架增强了语言障碍患者的沟通和可访问性.
- 在各种应用中,LATTE为改进人机交互提供了基础.
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