Robust Visual Lips Feature Extraction Method for Improved Visual Speech Recognition System | ||
| Engineering and Technology Journal | ||
| Article 5, Volume 36, 2A, February 2018, Pages 136-145 PDF (868.97 K) | ||
| DOI: 10.30684/etj.36.2A.4 | ||
| Authors | ||
| Mahmuod H. Mahmmed; Thamir R. Saeed; Wissam Ali* | ||
| Dept. of Electrical Engineering University of Technology, Baghdad, Iraq. | ||
| Abstract | ||
| Recently, automatic lips reading ALR acquired a significant interest among many researchers due to its adoption in many applications. One such application is in speech recognition system in noisy environment, where visual cue that contain some integral information added to the audio signal, as well as the way that person merges audio-visual stimulus to identify utterance. The unsolved part of this problem is the utterance classification using only the visual cues without the availability of acoustic signal of the talker's speech. By taking into considerations a set of frames from recorded video for a person uttering a word; a robust image processing technique is used to isolate the lips region, then suitable features are extracted that represent the mouth shape variation during speech. These features are used by the classification stage to identify the uttered word. This paper is solve this problem by introducing a new segmentation technique to isolate the lips region together with a set of visual features base on the extracted lips boundary which able to perform lips reading with significant result. A special laboratory is designed to collect the utterance of twenty six English letters from a multiple speakers which are adopted in this paper (UOTEletters corpus). Moreover; two type of classifier (using Numeral Virtual generalization (NVG) RAM and K nearest neighborhood KNN) where adopted to identify the talker’s utterance. The recognition performance for the input visual utterance when using NVG RAM is 94.679%, which is utilized for the first time in this work. While; 92.628% when KNN is utilize. | ||
| Keywords | ||
| visual speech; feature extraction; AV letters recognition; Classification | ||
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