Publication Details

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

ZEINALI Hossein, SAMETI Hossein, BURGET Lukáš, ČERNOCKÝ Jan, MAGHSOODI Nooshin and MATĚJKA Pavel. i-vector/HMM Based Text-dependent Speaker Verification System for RedDots Challenge. In: Proceedings of Interspeech 2016. San Francisco: International Speech Communication Association, 2016, pp. 440-444. ISBN 978-1-5108-3313-5. Available from: https://www.researchgate.net/publication/303895014_i-VectorHMM_Based_Text-Dependent_Speaker_Verification_System_for_RedDots_Challenge
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
Zeinali Hossein, Ph.D. (DCGM FIT BUT)
Sameti Hossein (SHARIF)
Burget Lukáš, doc. Ing., Ph.D. (DCGM FIT BUT)
Černocký Jan, prof. Dr. Ing. (DCGM FIT BUT)
Maghsoodi Nooshin (SHARIF)
Matějka Pavel, Ing., Ph.D. (DCGM FIT BUT)
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
Conference
Interspeech Conference, San Francisco, US
ISBN
978-1-5108-3313-5
Publisher
International Speech Communication Association
Place
San Francisco, US
DOI
10.21437/Interspeech.2016-1174
UT WoS
000409394400093
EID Scopus
2-s2.0-84994259825
BibTeX 
@INPROCEEDINGS{FITPUB11268,
   author = "Hossein Zeinali and Hossein Sameti and Luk\'{a}\v{s} Burget and Jan \v{C}ernock\'{y} and Nooshin Maghsoodi and Pavel Mat\v{e}jka",
   title = "i-vector/HMM Based Text-dependent Speaker Verification System for RedDots Challenge",
   pages = "440--444",
   booktitle = "Proceedings of Interspeech 2016",
   year = 2016,
   location = "San Francisco, US",
   publisher = "International Speech Communication Association",
   ISBN = "978-1-5108-3313-5",
   doi = "10.21437/Interspeech.2016-1174",
   language = "english",
   url = "https://www.fit.vut.cz/research/publication/11268"
}
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