A comparative study of English viseme recognition methods and algorithms

Abstract

An elementary visual unit – the viseme is concerned in the paper in the context of preparing the feature vector as a main visual input component of Audio-Visual Speech Recognition systems. The aim of the presented research is a review of various approaches to the problem, the implementation of algorithms proposed in the literature and a comparative research on their effectiveness. In the course of the study an optimal feature vector construction and an appropriate selection of the classifier were sought. The experimental research was conducted on the basis of a spoken corpus in which speech was represented both acoustically and visually. The extracted features represented three types: geometrical, textural and mixed ones. The features were processed employing the classification algorithms based on Hidden Markov Models and Sequential Minimal Optimization. Tests were carried out employing the processed video material recorded with English native speakers who read specially prepared list of commands. The obtained results are discussed in the paper.