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KARAFIÁT, M. VESELÝ, K. ČERNOCKÝ, J. PROFANT, J. NYTRA, J. HLAVÁČEK, M. PAVLÍČEK, T.
Originální název
Analysis of X-Vectors for Low-Resource Speech Recognition
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
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.
Klíčová slova
speech recognition, adaptation, x-vectors, data augmentation, robustness
Autoři
KARAFIÁT, M.; VESELÝ, K.; ČERNOCKÝ, J.; PROFANT, J.; NYTRA, J.; HLAVÁČEK, M.; PAVLÍČEK, T.
Vydáno
6. 6. 2021
Nakladatel
IEEE Signal Processing Society
Místo
Toronto, Ontario
ISBN
978-1-7281-7605-5
Kniha
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Strany od
6998
Strany do
7002
Strany počet
5
URL
https://www.fit.vut.cz/research/publication/12525/
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/" }