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This paper presents a silent speech recognition technique based on facial muscle activity and video, without evaluating any voice signals. This research examines the use of facial surface electromyogram (SEMG) to identify unvoiced vowels and vision-based technique to classify unvoiced consonants. The moving root mean square (RMS) of SEMG signals of four facial muscles is used to segment the signals...
This paper describes a new technique for recognizing speech using visual speech information. The video data of the speaker's mouth is represented using grayscale images named as motion history image (MHI). MHI is generated by applying accumulative image differencing on the frames of the video to implicitly represent the temporal information of the mouth movement. The MHIs are decomposed into wavelet...
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