Voiceless Arabic vowels recognition using facial EMG
Voiceless Arabic vowels recognition using facial EMG
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DOI：
10.1007/s11517-011-0751-1
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发表时间：
2011-07-01
影响因子：
3.2
通讯作者：
Saifan, Rasha
中科院分区：
文献类型：
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作者：
Fraiwan, Luay;Lweesy, Khaldon;Saifan, Rasha
本研究尝试利用脸部肌电讯号来辨识阿拉伯语母音，以应用于语音障碍者及人机介面。之所以选择元音，是因为它们是阿拉伯语中最难识别的字母。20名受试者（7名女性和13名男性）被要求以随机顺序连续发音三个阿拉伯元音。面部肌电信号是通过三个通道记录的，这些通道来自负责说话的三个主要面部肌肉。然后对肌电信号进行预处理，以消除噪声和干扰信号。基于移动标准差窗口，实现了对每个元音的时间事件的分割。分割过程的准确性被认为是94%。元音的识别是通过提取特征的肌电信号在三个域：时间，频谱和时间频率使用小波包变换。最后，使用WEKA软件中实现的不同分类方法对提取的特征进行分类。具有时间频率特征的随机森林分类器表现出最好的性能，使用10倍交叉验证评估的准确率为77%。
This work attempts to recognize the Arabic vowels based on facial electromyograph (EMG) signals, to be used for people with speech impairment and for human computer interface. Vowels were selected since they are the most difficult letters to recognize by people in Arabic language. Twenty subjects (7 females and 13 males) were asked to pronounce three Arabic vowels continuously in a random order. Facial EMG signals were recorded over three channels from the three main facial muscles that are responsible for speech. The EMG signals are then preprocessed to eliminate noise and interference signals. Segmentation procedure was implemented to extract the time event that corresponds to each vowel based on a moving standard deviation window. The accuracy of the segmentation procedure was found to be 94%. The recognition of the vowels was carried out by extracting features from the EMG in three domains: the temporal, the spectral, and the time frequency using the wavelet packet transform. Classification of the extracted features was then finally performed using different classification methods implemented in the WEKA software. The random forest classifier with time frequency features showed the best performance with an accuracy of 77% evaluated using a 10-fold cross-validation.