Two novel visual voice activity detectors based on appearance models and retinal filtering

In this paper we present two novel methods for visual voice activity detection (V-VAD) which exploit the bimodality of speech (i.e. the coherence between speaker's lips and the resulting speech). The first method uses appearance parameters of a speaker's lips, obtained from an active appearance model (AAM). An HMM then dynamically models the change in appearance over time. The second method uses a retinal filter on the region of the lips to extract the required parameter. A corpus of a single speaker is applied to each method in turn, where each method is used to classify voice activity as speech or non speech. The efficiency of each method is evaluated individually using receiver operating characteristics and their respective performances are then compared and discussed. Both methods achieve a high correct silence detection rate for a small false detection rate.