Detail publikace
Non-Parametric Bayesian Subspace Models for Acoustic Unit Discovery
ONDEL YANG, L. YUSUF, B. BURGET, L. SARAÇLAR, M.
Originální název
Non-Parametric Bayesian Subspace Models for Acoustic Unit Discovery
Typ
článek v časopise ve Web of Science, Jimp
Jazyk
angličtina
Originální abstrakt
This work investigates subspace non-parametricmodels for the task of learning a set of acoustic units fromunlabeledspeech recordings. We constrain the base-measure of a Dirichlet-Process mixture with a phonetic subspaceestimated from othersource languagesto build an educated prior, thereby forcing thelearned acoustic units to resemble phones of known source languages.Two types of models are proposed: (i) the Subspace HMM(SHMM) which assumes that the phonetic subspace is the same forevery language, (ii) the Hierarchical-Subspace HMM (H-SHMM)which relaxes this assumption and allows to have a languagespecificsubspace estimated on the unlabeled target data. Thesemodels are applied on 3 languages: English, Yoruba and Mboshiand they are compared with various competitive acoustic unitsdiscovery baselines. Experimental results show that both subspacemodels outperform other systems in terms of clustering quality andsegmentation accuracy. Moreover, we observe that the H-SHMMprovides results superior to the SHMM supporting the idea thatlanguage-specific priors are preferable to language-agnostic priorsfor acoustic unit discovery.
Klíčová slova
Unsupervised learning, non- parametricBayesian models, acoustic unit discovery
Autoři
ONDEL YANG, L.; YUSUF, B.; BURGET, L.; SARAÇLAR, M.
Vydáno
3. 5. 2022
ISSN
2329-9290
Periodikum
IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH AND LANGUAGE PROCESSING
Ročník
30
Číslo
5
Stát
Spojené státy americké
Strany od
1902
Strany do
1917
Strany počet
16
URL
BibTex
@article{BUT178412,
author="ONDEL YANG, L. and YUSUF, B. and BURGET, L. and SARAÇLAR, M.",
title="Non-Parametric Bayesian Subspace Models for Acoustic Unit Discovery",
journal="IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH AND LANGUAGE PROCESSING",
year="2022",
volume="30",
number="5",
pages="1902--1917",
doi="10.1109/TASLP.2022.3171975",
issn="2329-9290",
url="https://ieeexplore.ieee.org/document/9767690"
}
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