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AI突破:脑电波转化为语音助力瘫痪患者发声

加州大学伯克利分校和旧金山分校研究团队开发出革命性AI系统,能够实时将瘫痪患者的脑电波转化为自然语音,且保留患者原声音特征。该技术通过神经义肢采集大脑运动皮层的神经活动,并由AI将其解码为语音,处理延迟仅1秒,较2023年的研究成果缩短了7秒。

系统支持多种脑信号采集设备,包括高密度电极阵列、穿透脑表面的微电极以及非侵入式表面肌电图传感器。研究团队使用患者受伤前的声音样本训练文本到语音模型,通过让患者默读屏幕单词来映射神经活动与语言的关系。

研究人员表示,该技术能显著改善瘫痪及ALS患者的生活质量,未来将致力于加快AI处理速度并使输出语音更具表现力。

UC Berkeley Engineering

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AI突破:脑电波转化为语音助力瘫痪患者发声

加州大学伯克利分校和旧金山分校研究团队开发出革命性AI系统,能够实时将瘫痪患者的脑电波转化为自然语音,且保留患者原声音特征。该技术通过神经义肢采集大脑运动皮层的神经活动,并由AI将其解码为语音,处理延迟仅1秒,较2023年的研究成果缩短了7秒。

系统支持多种脑信号采集设备,包括高密度电极阵列、穿透脑表面的微电极以及非侵入式表面肌电图传感器。研究团队使用患者受伤前的声音样本训练文本到语音模型,通过让患者默读屏幕单词来映射神经活动与语言的关系。

研究人员表示,该技术能显著改善瘫痪及ALS患者的生活质量,未来将致力于加快AI处理速度并使输出语音更具表现力。

UC Berkeley Engineering

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