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

http://www.sciencedirect.com/science/article/pii/S0885230816303199

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"
}