SNR-invariant multitask deep neural networks for robust speaker verification

Abstract

A major challenge in speaker verification is to achieve low error rates under noisy environments. We observed that background noise in utterances will not only enlarge the speaker-dependent i-vector clusters but also shift the clusters, with the amount of shift depending on the signal-to-noise ratio (SNR) of the utterances. To overcome this SNR-dependent clustering phenomenon, we propose two deep neural network (DNN) architectures: hierarchical regression DNN (H-RDNN) and multitask DNN (MT-DNN). The H-RDNN is formed by stacking two regression DNNs in which the lower DNN is trained to map noisy i-vectors to their respective speaker-dependent cluster means of clean i-vectors and the upper DNN aims to regularize the outliers that cannot be denoised properly by the lower DNN. The MT-DNN is trained to denoise i-vectors (main task) and classify speakers (auxiliary task). The network leverages the auxiliary task to retain speaker information in the denoised i-vectors. Experimental results suggest that these two DNN architectures together with the PLDA backend significantly outperform the multicondition PLDA model and mixtures of PLDA, and that multitask learning helps to boost verification performance.