Real-time language independent lip synchronization method using a genetic algorithm

Lip synchronization is a method for the determination of the mouth and tongue motion during a speech. It is widely used in multimedia productions, and real time implementation is opening application possibilities in multimodal interfaces. We present an implementation of real time, language independent lip synchronization based on the classification of the speech signal, represented by MFCC vectors, into visemes using neural networks (NNs). Our implementation improves real time lip synchronization by using a genetic algorithm for obtaining a near optimal NN topology. The automatic NN configuration with genetic algorithms eliminates the need for tedious manual NN design by trial and error and considerably improves the viseme classification results. Moreover, by the direct usage of visemes as the basic unit of the classification, computation overhead is reduced, since only visemes are used for the animation of the face. The results are obtained in comprehensive validation of the system using three different evaluation methods, two objective and one subjective. The obtained results indicate very good lip synchronization quality in real time conditions and for different languages, making the method suitable for a wide range of applications.