Author: Yao, Qi
Title: SNR-invariant deep neural networks using multi-task learning for robust I-vector speaker verification
Advisors: Mak, M. W. (EIE)
Degree: M.Sc.
Year: 2018
Subject: Hong Kong Polytechnic University -- Dissertations
Automatic speech recognition
Speech processing systems
Department: Department of Electronic and Information Engineering
Pages: xvi, 92 pages : color illustrations
Language: English
Abstract: Text-independent speaker verification (SV) is a binary classification task that aims to verify the identity of speakers through analyzing and classifying their voices. The i-vector feature representation together with the probabilistic linear discriminant analysis (PLDA) backend have achieved state-of-the-art performance. However, it is still challenging when the i-vector/PLDA framework is applied to real-world noisy environments. This is because i-vectors represent all kinds of variabilities in the total variability space. This dissertation shows that i-vectors form clusters according to the SNR level of utterances. In light of this SNR-dependent clustering phenomenon, we propose three deep neural networks (DNN) to compensate for the channel-and SNR-variabilities directly in the i-vector space. These three DNNs are named as Regression DNN (RDNN), Hierarchical Regression DNNs (H-RDNNs) and Multi-Task DNN (MT-DNN), respectively. The RDNN takes noisy i-vectors as input and maps them to speaker-dependent cluster means. The H-RDNNs are formed by stacking a second regression DNN on top of the RDNN. The second stage of the H-RDNN aims to regularize the outliers that cannot be denoised properly by the RDNN. The MT-DNN makes use of an extra speaker classification task as an auxiliary task to retain speaker information in the denoised i-vectors. The secondary task of the MT-DNN is trained with a primary (regression) task using an alternating-backpropagation algorithm. We found that among all DNN-based denoising models, the MT-DNN achieves the best performance for denoising the noisy i-vectors. Experiments based on NIST 2012 SRE suggest that DNN-based approaches together with the PLDA backend significantly outperforms the multi-condition PLDA model and mixture of PLDA models. Furthermore, the MT-DNN achieves considerable improvements with 23% reduction in EER and 9% reduction in minDCF on average in Common Condition (CC) 4 and 5, even in an SNR mismatch condition.
Rights: All rights reserved
Access: restricted access

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