RBF neural network mouth tracking for audio-visual speech recognition system

A great interest in the research of audio-visual speech recognition (AVSR) systems is driven by the increase in the number of multimedia applications that require robust speech recognition systems. The use of visual features in AVSR is justified by both the audio and visual modality of the speech generation and the need for features that are invariant to acoustic noise perturbation. The performance of the AVSR system relies on a robust set of visual features obtained from the accurate detection and tracking of the mouth region. Therefore the mouth tracking plays a major role in AVSR systems. This paper presents an improvement version of mouth tracking technique using radial basis function neural network (RBF NN) with its applications to AVSR systems. A modified extended Kalman filter (EKF) is used to adjust the parameters of the RBF NN. Simulation results have revealed good performance of the proposed method.