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Cascading appearance-based features for visual voice activity detection

The detection of voice activity is a challenging problem, especially 
when the level of acoustic noise is high. Most current 
approaches only utilise the audio signal, making them susceptible 
to acoustic noise. An obvious approach to overcome this 
is to use the visual modality. The current state-of-the-art visual 
feature extraction technique is one that uses a cascade of visual 
features (i.e. 2D-DCT, feature mean normalisation, interstep 
LDA). In this paper, we investigate the effectiveness of this 
technique for the task of visual voice activity detection (VAD), 
and analyse each stage of the cascade and quantify the relative 
improvement in performance gained by each successive stage. 
The experiments were conducted on the CUAVE database and 
our results highlight that the dynamics of the visual modality 
can be used to good effect to improve visual voice activity detection 
performance.