Publication detail

Non-Parametric Bayesian Subspace Models for Acoustic Unit Discovery

ONDEL YANG, L. YUSUF, B. BURGET, L. SARAÇLAR, M.

Original Title

Non-Parametric Bayesian Subspace Models for Acoustic Unit Discovery

Type

journal article in Web of Science

Language

English

Original Abstract

This work investigates subspace non-parametric models for the task of learning a set of acoustic units fromunlabeled speech recordings. We constrain the base-measure of a Dirichlet- Process mixture with a phonetic subspaceestimated from other source languagesto build an educated prior, thereby forcing the learned 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 for every language, (ii) the Hierarchical-Subspace HMM (H-SHMM) which relaxes this assumption and allows to have a languagespecific subspace estimated on the unlabeled target data. These models are applied on 3 languages: English, Yoruba and Mboshi and they are compared with various competitive acoustic units discovery baselines. Experimental results show that both subspace models outperform other systems in terms of clustering quality and segmentation accuracy. Moreover, we observe that the H-SHMM provides results superior to the SHMM supporting the idea that language-specific priors are preferable to language-agnostic priors for acoustic unit discovery.

Keywords

Unsupervised learning, non- parametricBayesian models, acoustic unit discovery

Authors

ONDEL YANG, L.; YUSUF, B.; BURGET, L.; SARAÇLAR, M.

Released

3. 5. 2022

ISBN

2329-9290

Periodical

IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH AND LANGUAGE PROCESSING

Year of study

30

Number

5

State

United States of America

Pages from

1902

Pages to

1917

Pages count

16

URL

https://ieeexplore.ieee.org/document/9767690

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"
}

Documents

ondel_ieee_acm-tslp2022_Non-Parametric_Bayesian_Subspace_Models_for_Acoustic_Unit_Discovery.pdf