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Updated: Mar 10, 2026
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Decoding Natural Behavior from Neuroethological Embedding
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一个神经语音解码框架,利用深度学习和语音合成
Xupeng Chen1, Ran Wang1, Amirhossein Khalilian-Gourtani2
1Electrical and Computer Engineering Department, New York University, Brooklyn, NY USA.
Nature machine intelligence
|March 9, 2026
概括
研究人员开发了一个深度学习框架,使用电皮质谱 (ECoG) 来从大脑信号中解码语音. 这种新的方法使大脑与计算机接口 (BCI) 的自然声音语音合成成为可能,帮助那些语音丧失的人.
科学领域:
- 神经科学是一个神经科学.
- 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:
- 通过脑电脑接口 (BCI) 恢复神经缺陷患者的语言能力至关重要,但具有挑战性.
- 困难包括有限的神经信号数据,复杂的数据和高维度.
- 现有的方法在实时应用和广泛应用方面存在局限性.
研究的目的:
- 提出一种新的深度学习框架,用于从神经信号解码人类语音.
- 为了使电皮质谱 (ECoG) 信号转化为语音参数,并合成自然听起来的语音.
- 为了促进实时神经假肢的发展,用于语音恢复.
主要方法:
- 开发了一个深度学习框架,包括一个ECoG解码器和一个可差异化的语音合成器.
- 使用语音对语音自动编码器与语音编码器和语音合成器生成参考语音参数.
- 训练模型使用ECoG信号和48名参与者的相应语音数据.
主要成果:
- 该框架成功地从ECoG信号中解码了语音,产生了自然的声音和可重现的语音.
- 在语音解码中实现了高相关性,即使是适合实时假肢的因果操作.
- 在左半球和右半球ECoG覆盖范围的参与者中,语音解码成功.
结论:
- 新的深度学习框架为神经语音解码提供了一个有希望的方法.
- 该系统产生自然语音的能力以及其在参与者之间可复制性突显了其潜力.
- 这项技术可以显著推进发言假肢的发展,以帮助因神经损伤而发言障碍的人.
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