A robust speech disorders correction system for Arabic language using visual speech recognition

In this Paper, we propose an automatic speech disorders recognition technique based on both speech and visual components analysis. First, we performed the pre-processing steps required for speech recognition then we chose the Mel-frequency cepstral coefficients (MFCC's) as features representing the speech signal. On the other hand, we studied the visual components based on lipsmovements analysis. We propose a new technique that integrates both the audio signal and the video signal analysis techniques for increasing the efficiency of the automated speech disorders recognition systems. The main idea is to detect the motion features from a series of lipsimages. A new technique for lips movement detection is proposed. Finally we use the multi-layer neural network as a classifier for both speech and visual features. We propose a new technique for speech disorders correction systems, especially for Arabic language. Practical experiments showed that our system is useful when dealing with Arabic language speech disorders.