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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