内在语音解码:一个全面的审查
Maram Fahaad Almufareh1,2, Sumaira Kausar3, Mamoona Humayun4
1Department of Information Systems, College of Computer and Information Sciences, Jouf University, Riyadh, Saudi Arabia.
Wiley interdisciplinary reviews. Cognitive science
|November 2, 2025
概括
内部语音解码使用神经信号来帮助严重障碍者进行沟通. 机器学习的进步表明,通过谨慎的方法和道德考虑,准确的无声语音识别具有前景.
科学领域:
- 神经科学和计算机科学,专注于脑计算机接口中的机器学习应用.
背景情况:
- 内部语音解码旨在从神经信号解释无声语音,为残疾人提供交流选择.
- 现有的研究探讨了各种记录方式和机器学习策略,以推断隐藏的语音.
研究的目的:
- 审查当前的内在语音解码技术,包括记录方法和机器学习算法.
- 讨论影响解码精度的因素,如词汇大小,传感器质量和数据.
- 检查与大脑数据隐私和用户信任相关的伦理,社会和监管挑战.
主要方法:
- 对非侵入性 (EEG) 和侵入性 (电皮质谱) 记录方式的审查.
- 机器学习策略的分析,包括线性判别分析,深度卷积网络和EEG-fMRI融合.
- 专注于公共数据集的想象或表达的演讲,以说明传感器质量,数据大小和域调整的影响.
主要成果:
- 在受控条件下的小词汇量可以在内在语音解码中达到合理的准确性.
- 基于上下文的方法可以进一步完善解码结果.
- 有效的内在语音识别需要高质量的预处理,对象校准和知情的建模选择.
结论:
- 内部语音解码技术具有很大的潜力,可以在受损人群中提供通信辅助.
- 方法标准,包括预处理和校准,对于可靠的解码至关重要.
- 应对道德和社会挑战对于负责任的开发和采用这项技术至关重要.
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