Retrieval of unintelligible speech is a basic need for speech impaired and is under research for several decades. But retrieval of random words from thoughts needs a substantial and consistent approach. This work focuses on the preliminary steps of retrieving vowels from Electroencephalography (EEG) signals acquired while speaking and imagining of speaking a consonant–vowel–consonant (CVC) word. The process, referred to as Speech imagery is imagining of speaking to oneself silently in the mind. Speech imagery is a form of mental imagery. Brain connectivity estimators such as EEG coherence, Partial Directed Coherence, Directed Transfer Function and Transfer Entropy have been used to estimate the concurrency and causal dependence (direction and strength) between different brain regions. From brain connectivity results it has been observed that the left frontal and left temporal electrodes were activated for speech and speech imagery processes. These brain connectivity estimators have been used for training Recurrent Neural Networks (RNN) and Deep Belief Networks (DBN) for identifying the vowel from the subject’s thought. Though the accuracy level was found to be varying for each vowel while speaking and imagining of speaking the CVC word, the overall classification accuracy was found to be 72% while using RNN whereas a classification accuracy of 80% was observed while using DBN. DBN was found to outperform RNN in both the speech and speech imagery processes. Thus, the combination of brain connectivity estimators and deep learning techniques appear to be effective in identifying the vowel from EEG signals of subjects’ thought.

中文翻译：

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从基于语音图像的脑电信号中识别辅音-元音-辅音单词中的元音

检索难以理解的语音是语音受损的基本需求，并且正在研究数十年。但是从思想中检索随机单词需要一种实质性且一致的方法。这项工作着重于从说话时获得的脑电图（EEG）信号中检索元音的初步步骤，以及想象说出辅音-元音-辅音（CVC）单词的初步步骤。这个过程被称为“语音图像”，是想象在脑海中默默地对自己说话。言语意象是心理意象的一种形式。脑电连通性估计器（例如EEG相干性，部分定向相干性，定向传递函数和传递熵）已用于估计不同大脑区域之间的并发性和因果关系（方向和强度）。从大脑的连通性结果可以看出，左侧额叶和左侧颞电极被激活以进行语音和语音成像过程。这些大脑连通性估算器已用于训练递归神经网络（RNN）和深层信念网络（DBN），以从受试者的思想中识别元音。尽管发现说和想象说CVC单词时每个元音的准确度水平有所不同，但使用RNN时，总体分类准确度为72％，而使用DBN时，分类准确度为80％。在语音和语音图像处理过程中，发现DBN优于RNN。因此，大脑连通性估计器和深度学习技术的结合似乎可以有效地从受试者思想的脑电信号中识别元音。