Visual Speech Detection Using an Unsupervised Learning Framework

This paper presents an unsupervised learning framework for visual speech detection. Bimodal GMM is used to model visual features, i.e., mouth region intensity, which varies during speech. Variation in the mouth region intensity is used for visual speech and non-speech classification. The GMM parameters are estimated using the EM algorithm. Performance of the proposed algorithm is evaluated using a dataset consisting of 14 video clips containing almost 20, 000 frames. Performance of the proposed algorithm is also compared with existing state-of-the-art. Experimental results show that the proposed method achieves high detection and low false alarm rates.