Detail publikace
Text-dependent speaker verification based on i-vectors, Neural Networks and Hidden Markov Models
ZEINALI, H. SAMETI, H. BURGET, L. ČERNOCKÝ, J.
Originální název
Text-dependent speaker verification based on i-vectors, Neural Networks and Hidden Markov Models
Typ
článek v časopise ve Web of Science, Jimp
Jazyk
angličtina
Originální abstrakt
Inspired by the success of Deep Neural Networks (DNN) in text-independent speaker recognition, we have recently demonstrated that similar ideas can also be applied to the text-dependent speaker verification task. In this paper, we describe new advances with our state-of-the-art i-vector based approach to text-dependent speaker verification, which also makes use of different DNN techniques. In order to collect sufficient statistics for i-vector extraction, different frame alignment models are compared such as GMMs, phonemic HMMs or DNNs trained for senone classification. We also experiment with DNN based bottleneck features and their combinations with standard MFCC features. We experiment with few different DNN configurations and investigate the importance of training DNNs on 16 kHz speech. The results are reported on RSR2015 dataset, where training material is available for all possible enrollment and test phrases. Additionally, we report results also on more challenging RedDots dataset, where the system is built in truly phrase-independent way.
Klíčová slova
Deep Neural Network; Text-dependent; Speaker verification; i-Vector; Frame alignment; Bottleneck features
Autoři
ZEINALI, H.; SAMETI, H.; BURGET, L.; ČERNOCKÝ, J.
Vydáno
12. 5. 2017
ISSN
0885-2308
Periodikum
COMPUTER SPEECH AND LANGUAGE
Ročník
2017
Číslo
46
Stát
Spojené království Velké Británie a Severního Irska
Strany od
53
Strany do
71
Strany počet
19
URL
BibTex
@article{BUT144474,
author="Hossein {Zeinali} and Hossein {Sameti} and Lukáš {Burget} and Jan {Černocký}",
title="Text-dependent speaker verification based on i-vectors, Neural Networks and Hidden Markov Models",
journal="COMPUTER SPEECH AND LANGUAGE",
year="2017",
volume="2017",
number="46",
pages="53--71",
doi="10.1016/j.csl.2017.04.005",
issn="0885-2308",
url="http://www.sciencedirect.com/science/article/pii/S0885230816303199"
}
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