Voice-quality Features for Deep Neural Network Based Speaker Verification Systems

Abstract

Jitter and shimmer are voice-quality features which have been successfully used to detect voice pathologies and classify different speaking styles. In this paper, we investigate the usefulness of such voice-quality features in neural-network based speaker verification systems. To combine these two sets of features, the cosine distance scores estimated from the two sets are linearly weighted to obtain a single, fused score. The fused score is used to accept/reject a given speaker. The experimental results carried out on Voxceleb-1 dataset demonstrate that the fusion of the cosine distance scores extracted from the mel-spectrogram and voice quality features provide a 15% relative improvement in Equal Error Rate (EER) compared to the baseline system which is based only on mel-spectrogram features.