i-Vector/HMM Based Text-Dependent Speaker Verification System for RedDots Challenge

Hossein Zeinali, Hossein Sameti, Lukáš Burget, Jan Černocký, Nooshin Maghsoodi, Pavel Matějka

Recently, a new data collection was initiated within the RedDots project in order to evaluate text-dependent and text-prompted speaker recognition technology on data from a wider speaker population and with more realistic noise, channel and phonetic variability. This paper analyses our systems built for RedDots challenge — the effort to collect and compare the initial results on this new evaluation data set obtained at different sites. We use our recently introduced HMM based i-vector approach, where, instead of the traditional GMM, a set of phone specific HMMs is used to collect the sufficient statistics for i-vector extraction. Our systems are trained in a completely phrase-independent way on the data from RSR2015 and Libri speech databases. We compare systems making use of standard cepstral features and their combination with neural network based bottle-neck features. The best results are obtained with a score-level fusion of such systems.

DOI: 10.21437/Interspeech.2016-1174

Cite as

Zeinali, H., Sameti, H., Burget, L., Černocký, J., Maghsoodi, N., Matějka, P. (2016) i-Vector/HMM Based Text-Dependent Speaker Verification System for RedDots Challenge. Proc. Interspeech 2016, 440-444.

author={Hossein Zeinali and Hossein Sameti and Lukáš Burget and Jan Černocký and Nooshin Maghsoodi and Pavel Matějka},
title={i-Vector/HMM Based Text-Dependent Speaker Verification System for RedDots Challenge},
booktitle={Interspeech 2016},