Publication Details

Analysis of X-Vectors for Low-Resource Speech Recognition

KARAFIÁT, M.; VESELÝ, K.; ČERNOCKÝ, J.; PROFANT, J.; NYTRA, J.; HLAVÁČEK, M.; PAVLÍČEK, T. Analysis of X-Vectors for Low-Resource Speech Recognition. In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Toronto, Ontario: IEEE Signal Processing Society, 2021. p. 6998-7002. ISBN: 978-1-7281-7605-5.
Czech title
Analýza x-vektorů pro rozpoznávání řeči s omezenými zdroji
Type
conference paper
Language
English
Authors
Karafiát Martin, Ing., Ph.D. (DCGM)
Veselý Karel, Ing., Ph.D. (DCGM)
Černocký Jan, prof. Dr. Ing. (DCGM)
Profant Ján, Ing.
Nytra Jiří, Bc.
HLAVÁČEK, M.
Pavlíček Tomáš, Ing.
URL
Keywords

speech recognition, adaptation, x-vectors, data augmentation, robustness

Abstract

The paper presents a study of usability of x-vectors for adaptation of automatic speech recognition (ASR) systems. Xvectors are Neural Network (NN)-based speaker embeddings recently proposed in speaker recognition (SR). They quickly replaced common i-vectors and became new state-of-the-art technique. Here, the same approach is adopted for ASR with the hope of similar outcome. All experiments were done on ASR for the latest IARPA MATERIAL evaluation running on Pashto language. Over 1% absolute improvement was observed with x-vectors over traditional i-vectors, even when the x-vector extractor was not trained on target Pashto data.

Published
2021
Pages
6998–7002
Proceedings
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
ISBN
978-1-7281-7605-5
Publisher
IEEE Signal Processing Society
Place
Toronto, Ontario
DOI
10.1109/ICASSP39728.2021.9414725
UT WoS
000704288407055
EID Scopus
2-s2.0-85115057300
BibTeX 
@inproceedings{BUT175794,
  author="KARAFIÁT, M. and VESELÝ, K. and ČERNOCKÝ, J. and PROFANT, J. and NYTRA, J. and HLAVÁČEK, M. and PAVLÍČEK, T.",
  title="Analysis of X-Vectors for Low-Resource Speech Recognition",
  booktitle="ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",
  year="2021",
  pages="6998--7002",
  publisher="IEEE Signal Processing Society",
  address="Toronto, Ontario",
  doi="10.1109/ICASSP39728.2021.9414725",
  isbn="978-1-7281-7605-5",
  url="https://www.fit.vut.cz/research/publication/12525/"
}
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