Result Details
HMM-Based Phrase-Independent i-Vector Extractor for Text-Dependent Speaker Verification
This article is describes a new HMM structure for text-dependentspeaker verification, which enabled the authors to use the potentialof the HMM to model time sequences along with the establishedi-vector technique.
Bottleneck features, DNN, hidden Markov model(HMM), i-vector, text-dependent speaker verification.
Abstract-The low-dimensional i-vector representation of speech segments is used in the state-of-the-art text-independent speaker verification systems. However, i-vectors were deemed unsuitable for the text-dependent task, where simpler and older speaker recognition approaches were found more effective. In this work,we propose a straightforward hiddenMarkovmodel (HMM) based extension of the i-vector approach, which allows i-vectors to be successfully applied to text-dependent speaker verification. In our approach, the Universal Background Model (UBM) for training phrase-independent i-vector extractor is based on a set of monophone HMMs instead of the standard Gaussian Mixture Model (GMM). To compensate for the channel variability, we propose to precondition i-vectors using a regularized variant of within-class covariance normalization, which can be robustly estimated in a phrase-dependent fashion on the small datasets available for the text-dependent task. The verification scores are cosine similarities between the i-vectors normalized using phrase-dependent s-norm. The experimental results on RSR2015 and RedDots databases confirm the effectiveness of the proposed approach, especially in rejecting test utterances with a wrong phrase. A simpleMFCC based i-vector/HMM system performs competitively when compared to very computationally expensive DNN-based approaches or the conventional relevance MAP GMM-UBM, which does not allow for compact speaker representations. To our knowledge, this paper presents the best published results obtained with a single system on both RSR2015 and RedDots dataset.
@article{BUT144447,
author="Hossein {Zeinali} and Hossein {Sameti} and Lukáš {Burget}",
title="HMM-Based Phrase-Independent i-Vector Extractor for Text-Dependent Speaker Verification",
journal="IEEE-ACM Transactions on Audio Speech and Language Processing",
year="2017",
volume="25",
number="7",
pages="1421--1435",
doi="10.1109/TASLP.2017.2694708",
issn="2329-9290",
url="http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7902120"
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IT4Innovations excellence in science, MŠMT, Národní program udržitelnosti II, LQ1602, start: 2016-01-01, end: 2020-12-31, completed
Zpracování, zobrazování a analýza multimediálních a 3D dat, BUT, Vnitřní projekty VUT, FIT-S-17-3984, start: 2017-03-01, end: 2020-02-29, completed