使用结构化概括错误指导大脑到发音解码器设计
概括
新的神经解码器显示了对解码自然主义语音的改进通用化. 使用潜伏神经因子和发射速度的模型优于基于尖端的解码器,推进了用于通信的脑计算机接口.
科学领域:
- 神经科学是一个神经科学.
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 当前的神经假体从神经活动中解码语音,在有限的词汇库中实现高率.
- 将这些解码器推广到具有大量词汇和多种背景的自然语言中仍然是一个挑战.
- 了解神经动态是提高解码器性能的关键.
研究的目的:
- 为了评估神经解码器对自然主义演讲的概括,使用一种新的语音单元级别测试.
- 为了比较使用尖列车的解码器的性能与潜在的神经特征 (因素和发射率).
- 通过动态系统 (LFADS) 调查隐性因子分析在捕获语音神经动态中的作用.
主要方法:
- 为神经解码器开发了一种声单元级概括测试.
- 模拟斑马的发声作为人类语音生产的模拟.
- 我们比较了三种解码器类型:尖峰列车,神经因素和发射率,用LFADS推断的因素/速率.
- 评估了使用随机保留和语音单元保留错误指标的概括.
主要成果:
- 传统的概括错误在所有解码器类型中都是相似的.
- 使用潜伏神经因子和发射速度的解码器在语音单元保持概括上显著超过了基于尖峰的解码器.
- 来自LFADS的特征更好地捕捉了从部分数据变化的灵活发音推断.
结论:
- 由LFADS推断的潜在神经动态对于强大的语音解码概括至关重要.
- 基于因素和速率的解码器为自然主义语音应用提供了更好的适应性.
- 未来的神经假肢的开发应纳入潜在的神经和发声动态,以增强沟通.
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