Publication Details
i-vector/HMM Based Text-dependent Speaker Verification System for RedDots Challenge
Sameti Hossein (FIT)
Burget Lukáš, doc. Ing., Ph.D. (DCGM)
Černocký Jan, prof. Dr. Ing. (DCGM)
Maghsoodi Nooshin (FIT)
Matějka Pavel, Ing., Ph.D. (DCGM)
text-dependent speaker verification, i-vector, HMM, RedDots challenge
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 phraseindependent 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 scorelevel fusion of such systems.
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 phraseindependent 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 scorelevel fusion of such systems.
@inproceedings{BUT131018,
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="Proceedings of Interspeech 2016",
year="2016",
pages="440--444",
publisher="International Speech Communication Association",
address="San Francisco",
doi="10.21437/Interspeech.2016-1174",
isbn="978-1-5108-3313-5",
url="https://www.researchgate.net/publication/303895014_i-VectorHMM_Based_Text-Dependent_Speaker_Verification_System_for_RedDots_Challenge"
}