An Algorithm to Identify Syllable from a Visual Speech Recognition System

This paper is to propose a highly efficient and reliable real time communication system for speech impaired people to communicate and converse in an effective manner. The main idea deals with an algorithm to identify the word from a visual speech input disregard with its acoustic property. The non-acoustic speech is captured through a source and given as an input in the form of image frames then classified to obtain the desired form of output. The input given in the visual form deals with mouth postures. The network is structured to identify the speech in form of syllables. Convolution Neural Network, a deep learning technique is used as its classifier. A database is created especially for this algorithm and are aligned within in the form of class and subsets.