Comparing audio and visual information for speech processing

Abstract

This paper examines the utility of audio-visual speech for the two related tasks of speech and speaker recognition. A study of the confusion that exists between speaker and speech elements was performed to show that principal component analysis (PCA) based visual speech is considerably better for the task of speaker recognition than for speech. Decision fusion speech and speaker recognition engines were also tested under various levels of acoustic degradation to find that the optimal fusion configuration for speaker recognition was substantially different than that for speech. These results highlight the problem of employing similar visual features for both speech and speaker recognition.