The performance of traditional voice activity detectors significantly deteriorates in the presence of highly nonstationary noise and transient interferences. One solution is to incorporate a video signal which is invariant to the acoustic environment. Although several voice activity detectors based on the video signal were recently presented, merely few detectors which are based on both the audio and the video signals exist in the literature to date. In this paper, we present an audio-visual voice activity detector and show that the incorporation of both audio and video signals is highly beneficial for voice activity detection.
The algorithm is based on a supervised learning procedure, and a labeled training data set is considered. The algorithm comprises a feature extraction procedure, where the features are designed to separate speech from nonspeech frames. Diffusion maps is applied separately and similarly to the features of each modality and builds a low dimensional representation. Using the new representation, we propose a measure for voice activity which is based on a supervised learning procedure and the variability between adjacent frames in time. The measures of the two modalities are merged to provide voice activity detection based on both the audio and the video signals. Experimental results demonstrate the improved performance of the proposed algorithm compared to state-of-the-art detectors.