论文标题
在基于脑电图的语音识别系统中，理解语音感知的效果
Understanding effect of speech perception in EEG based speech recognition systems
论文作者
论文摘要
与语音并行记录的脑电图（EEG）信号用于执行孤立且连续的语音识别。在讲话过程中，人们还听到他或她自己的演讲，这种讲话的看法也反映在记录的脑电图信号中。在本文中，我们研究了是否可以将这种语音感知组件与EEG信号分开，以设计基于EEG的更强大的语音识别系统。我们进一步证明了与从被动聆听并行记录的EEG信号并行记录的预测脑电图信号，反之亦然，归一化均方根误差（RMSE）非常低。我们最终使用与聆听，说话和改善先前的连接派时间分类（CTC）模型结果同时录制的EEG信号既孤立又连续的语音识别。
The electroencephalography (EEG) signals recorded in parallel with speech are used to perform isolated and continuous speech recognition. During speaking process, one also hears his or her own speech and this speech perception is also reflected in the recorded EEG signals. In this paper we investigate whether it is possible to separate out this speech perception component from EEG signals in order to design more robust EEG based speech recognition systems. We further demonstrate predicting EEG signals recorded in parallel with speaking from EEG signals recorded in parallel with passive listening and vice versa with very low normalized root mean squared error (RMSE). We finally demonstrate both isolated and continuous speech recognition using EEG signals recorded in parallel with listening, speaking and improve the previous connectionist temporal classification (CTC) model results demonstrated by authors in [1] using their data set.
