Publication Details
Speaker Recognition With Random Digit Strings Using Uncertainty Normalized HMM-Based i-Vectors
text-dependent speaker verification, uncertainty compensation, HMM, i-vector
In this paper, we combine Hidden Markov Models (HMMs) with i-vector extractors to
address the problem of text-dependent speaker recognition with random digit
strings. We employ digit-specific HMMs to segment the utterances into digits, to
perform frame alignment to HMM states and to extract Baum-Welch statistics. By
making use of the natural partition of input features into digits, we train
digit-specific i-vector extractors on top of each HMM and we extract
well-localized i-vectors, each modelling merely the phonetic content
corresponding to a single digit. We then examine ways to perform channel and
uncertainty compensation, and we propose a novel method for using the uncertainty
in the i-vector estimates. The experiments on RSR2015 part III show that the
proposed method attains 1.52% and 1.77% Equal Error Rate (EER) for male and
female respectively, outperforming state-of-the-art methods such as x-vectors,
trained on vast amounts of data. Furthermore, these results are attained by
a single system trained entirely on RSR2015, and by a simple score-normalized
cosine distance. Moreover, we show that the omission of channel compensation
yields only a minor degradation in performance, meaning that the system attains
state-of-the-art results even without recordings from multiple handsets per
speaker for training or enrolment. Similar conclusions are drawn from our
experiments on the RedDots corpus, where the same method is evaluated on phrases.
Finally, we report results with bottleneck features and show that further
improvement is attained when fusing them with spectral features.
@article{BUT159984,
author="Nooshin {Maghsoodi} and Hossein {Sameti} and Hossein {Zeinali} and Themos {Stafylakis}",
title="Speaker Recognition With Random Digit Strings Using Uncertainty Normalized HMM-Based i-Vectors",
journal="IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH AND LANGUAGE PROCESSING",
year="2019",
volume="2019",
number="11",
pages="1815--1825",
doi="10.1109/TASLP.2019.2928143",
issn="2329-9290",
url="https://ieeexplore.ieee.org/document/8759963?source=authoralert"
}