Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/77313
Title: DNN-Based score calibration with multitask learning for noise robust speaker verification
Authors: Tan, Z 
Mak, MW 
Mak, BKW
Keywords: Deep learning
Multi-task learning
Noise robustness
Score calibration
Speaker verification
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE/ACM transactions on audio, speech, and language processing, 2018, v. 26, no. 48249870, p. 700-712 How to cite?
Journal: IEEE/ACM transactions on audio, speech, and language processing 
Abstract: This paper proposes and investigates several deep neural network (DNN) based score compensation, transformation, and calibration algorithms for enhancing the noise robustness of i-vector speaker verification systems. Unlike conventional calibration methods where the required score shift is a linear function of SNR or log-duration, the DNN approach learns the complex relationship between the score shifts and the combination of i-vector pairs and uncalibrated scores. Furthermore, with the flexibility of DNNs, it is possible to explicitly train a DNN to recover the clean scores without having to estimate the score shifts. To alleviate the overfitting problem, multitask learning is applied to incorporate auxiliary information such as SNRs and speaker ID of training utterances into the DNN. Experiments on NIST 2012 SRE show that score calibration derived from multitask DNNs can improve the performance of the conventional score-shift approch significantly, especially under noisy conditions.
URI: http://hdl.handle.net/10397/77313
ISSN: 2329-9290
EISSN: 2329-9304
DOI: 10.1109/TASLP.2018.2791105
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