在ECoG中用受过公开演讲训练的变压器来解码隐藏语音的可行性
Shuji Komeiji1, Takumi Mitsuhashi2, Yasushi Iimura2
1Department of Electronic and Information Engineering, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei-shi, Tokyo, 184-8588, Japan.
Scientific reports
|May 20, 2024
概括
研究人员开发了一个变压器模型,使用电皮质图 (ECoG) 数据来解码隐藏的语音. 用公开语音训练模型显著提高了隐蔽语音解码的准确性,解决了数据收集的挑战.
科学领域:
- 神经科学是一个神经科学.
- 计算机科学 计算机科学
- 生物医学工程 生物医学工程
背景情况:
- 大脑计算机接口 (BCI) 旨在使用侵入性电皮质图 (ECoG) 测量来解码语音信号.
- 解码想象 (隐蔽) 语音与公开或感知语音相比,是一个重大的挑战.
研究的目的:
- 为了研究使用变压器神经网络从隐蔽语音解码日本句子的可行性.
- 评估公开演讲的ECoG数据是否可用于训练解码隐藏演讲的模型.
主要方法:
- 16名患者的ECoG数据在日本语句的公开和隐蔽演讲中被记录下来.
- 一个变压器神经网络被用来解码隐藏语音中的文本句子.
- 该模型经过训练和测试,使用隐藏语音数据和公开语音数据来训练隐藏语音解码.
主要成果:
- 在秘密演讲上训练的变压器模型实现了46.6%的代码错误率 (TER) 来解码秘密演讲.
- 一个在公开语音数据上训练的模型在解码隐藏语音方面获得了46.3%的可比TER.
- 这些结果表明,公开语音数据可以有效地训练用于隐藏语音解码的模型.
结论:
- 通过利用易于获得的公开语音数据,可以减轻获得足够的隐藏语音BCI培训数据的挑战.
- 在培训中使用公开语音数据可以提高隐藏语音解码模型的性能.
- 这种方法为推进语音BCI技术提供了实际解决方案,特别是用于无声通信应用.
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