Designing Advanced Geometric Features for Automatic Russian Visual Speech Recognition

Abstract

The use of video information plays an increasingly important role for automatic speech recognition. Nowadays, audio-only based systems have reached a certain accuracy threshold and many researchers see a solution to the problem in the use of visual modality to obtain better results. Despite the fact that audio modality of speech is much more representative than video, their proper fusion can improve both quality and robustness of the entire recognition system that was proved in practice by many researches. However, no agreement between researchers on the optimal set of visual features was reached. In this paper, we investigate this issue in more detail and propose advanced geometry-based visual features for automatic Russian lip-reading system. The experiments were conducted using collected HAVRUS audio-visual speech database. The average viseme recognition accuracy of our system trained on the entire corpus is 40.62%. We also tested the main state-of-the-art methods for visual speech recognition, applying them to continuous Russian speech with high-speed recordings (200 frames per seconds).