Lip movement and speech synchronization detection based on multimodal shift-invariant dictionary

In order to solve the issue of ignoring the successive and dynamic lip motion information in traditional audio-visual speech synchrony analysis models, a novel method based on shift-invariant learned dictionary is presented. In this method, sparse representation with shift-invariant dictionary is introduced to analyze the bimodal structure of articulation. The learned dictionary is obtained based on the audio-visual coherence dictionary learning algorithm, and the dynamic correlation between voice and lip motion of diverse syllable or word is represented as a pattern by this audio-visual coherence atom. According to these utterance patterns, an original audio-visual synchronization score measuring scheme is proposed. The results of the experiment on four different asynchronous data show the good performance of the method.