SNR-Invariant Multitask Deep Neural Networks for Robust Speaker Verification (original) (raw)

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 multi-condition PLDA model and mixtures of PLDA, and that multi-task learning helps to boost verification performance.