AN EFFICIENT TRANSFORMER-BASED MODEL FOR VOICE ACTIVITY DETECTION

Voice Activity Detection (VAD) aims to distinguish, at a given time, between desired speech and non-speech. Although many state-of-the-art approaches for increasing the performance of VAD have been proposed, they are still not robust enough to be applied under adverse noise conditions with low Signal-to-Noise Ratio (SNR). To deal with this issue, we propose a novel transformer-based architecture for VAD with reduced computational complexity by implementing efficient depth-wise convolutions on feature patches. The proposed model, named Tr-VAD, demonstrates better performance compared to baseline methods from the literature in a variety of scenarios considered with the smallest possible number of parameters. The results also indicate that the use of a combination of Audio Fingerprinting (ARP) features with Tr-VAD can guarantee better performance.