Analysis of X-Vectors for Low-Resource Speech Recognition
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F21%3APU142917" target="_blank" >RIV/00216305:26230/21:PU142917 - isvavai.cz</a>
Result on the web
<a href="https://www.fit.vut.cz/research/publication/12525/" target="_blank" >https://www.fit.vut.cz/research/publication/12525/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICASSP39728.2021.9414725" target="_blank" >10.1109/ICASSP39728.2021.9414725</a>
Alternative languages
Result language
angličtina
Original language name
Analysis of X-Vectors for Low-Resource Speech Recognition
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
ISBN
978-1-7281-7605-5
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
6998-7002
Publisher name
IEEE Signal Processing Society
Place of publication
Toronto, Ontario
Event location
Toronto, Canada
Event date
Jun 6, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000704288407055