Conference paper

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
Publication language:english
Original title:i-vector/HMM Based Text-dependent Speaker Verification System for RedDots Challenge
Title (cs):Systém pro ověřování mluvčího závislý na textu založený na kombinaci i-vektorů a HMM pro RedDots Challenge
Pages:440-444
Proceedings:Proceedings of Interspeech 2016
Conference:Interspeech 2016
Place:San Francisco, US
Year:2016
URL:https://www.researchgate.net/publication/303895014_i-VectorHMM_Based_Text-Dependent_Speaker_Verification_System_for_RedDots_Challenge
ISBN:978-1-5108-3313-5
Publisher:International Speech Communication Association
URL:http://www.fit.vutbr.cz/research/groups/speech/publi/2016/zeinali_interspeech2016_IS161174.pdf [PDF]
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Keywords
text-dependent speaker verification, i-vector, HMM, RedDots challenge
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.
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.
BibTeX:
@INPROCEEDINGS{
   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},
   language = {english},
   url = {http://www.fit.vutbr.cz/research/view_pub.php?id=11268}
}

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