Supervised/Unsupervised Voice Activity Detectors for Text- dependent Speaker Recognition on the RSR2015 Corpus

Voice activity detection, i.e., discrimination of the speech/nonspeech segments in a speech signal, is an important enabling technology for a variety of speech-based applications including the speaker recognition. In this work we provide a performance evaluation of the following supervised and unsupervised VAD algorithms in the context of text-dependent speaker recognition on the RSR2015 (Robust Speaker Recognition 2015) task : Energy-based VAD with and without hangover scheme and endpoint detection, vector quantizationbased VAD, Gaussian mixtures model (GMM)-based VAD (both supervised and unsupervised way), and sequential GMM-based VAD. Experimental results show that both the supervised and unsupervised GMM-based VADs perform better than the other VAD algorithms. Considering all three evaluation metrics (equal error rate, old (SRE 2008) and new (SRE 2010) normalized detection cost functions) unsupervised GMM-based VAD performed the best.