大脑到文本解码与上下文意识的神经表示和大型语言模型
Jingyuan Li1, Trung Le2, Chaofei Fan3
1University of Washington, 1410 NE Campus Pkwy, Seattle, 98195, UNITED STATES.
Journal of neural engineering
|August 12, 2025
概括
这项研究引入了二音声,这些声学单元捕获语音过渡,以改善神经活动的语音解码. 这种上下文意识的方法增强了语言障碍患者的沟通恢复.
科学领域:
- 神经科学是一个神经科学.
- 语音技术 语言技术
- 生物医学工程 生物医学工程
背景情况:
- 通过神经解码恢复语言障碍患者的沟通是关键的研究领域.
- 以前的方法使用了音符,忽略了依赖上下文的神经活动,导致低于最佳的语音解码.
- 情境感知建模对于提高脑到文本系统的准确性至关重要.
研究的目的:
- 提出和评估二音声作为一个文本意识的目标,用于将神经活动解码为语音.
- 通过一种新的分裂与征服策略,将二音声集成到现有的音符解码框架中.
- 通过利用来自双音的增强语境信息来提高语音解码性能.
主要方法:
- 开发了一种新的分裂与征服策略,将二音声集成到音符解码框架中.
- 通过边缘化对二音分布进行模拟的音调分布,以增强语境.
- 评估了Brain-to-Text 2024基准数据集的方法.
主要成果:
- 实现了 15.34% 的最先进的语音错误率 (PER),超过了 16.62% 的单音解码 PER.
- 当与微调的大型语言模型 (LLM) 结合使用时,该方法产生了5.77%的词错误率 (WER).
- 显著超过了领先的基准方法8.93%的WER.
结论:
- 基于双音声的解码提供了一种优越的,对上下文有意识的方法,用于将神经活动转化为语音.
- 这种方法有效地平衡了语境丰富性与可管理的复杂性,用于语音到文本的转换.
- 拟议的方法代表了用于语音恢复的大脑计算机接口的重大进步.
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