Publication Details

i-vector/HMM Based Text-dependent Speaker Verification System for RedDots Challenge

ZEINALI, H.; SAMETI, H.; BURGET, L.; ČERNOCKÝ, J.; MAGHSOODI, N.; MATĚJKA, P. i-vector/HMM Based Text-dependent Speaker Verification System for RedDots Challenge. In Proceedings of Interspeech 2016. San Francisco: International Speech Communication Association, 2016. p. 440-444. ISBN: 978-1-5108-3313-5.
Czech title
Systém pro ověřování mluvčího závislý na textu založený na kombinaci i-vektorů a HMM pro RedDots Challenge
Type
conference paper
Language
English
Authors
URL
Keywords

text-dependent speaker verification, i-vector, HMM, RedDots challenge

Abstract

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.

Annotation

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.

Published
2016
Pages
440–444
Proceedings
Proceedings of Interspeech 2016
ISBN
978-1-5108-3313-5
Publisher
International Speech Communication Association
Place
San Francisco
DOI
UT WoS
000409394400093
EID Scopus
BibTeX
@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"
}
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