Non-Parametric Bayesian Subspace Models for Acoustic Unit Discovery

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F22%3APU144939" target="_blank" >RIV/00216305:26230/22:PU144939 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9767690" target="_blank" >https://ieeexplore.ieee.org/document/9767690</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TASLP.2022.3171975" target="_blank" >10.1109/TASLP.2022.3171975</a>

Result language
angličtina
Original language name
Non-Parametric Bayesian Subspace Models for Acoustic Unit Discovery
Original language description
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.
Czech name
—
Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
<a href="/en/project/GX19-26934X" target="_blank" >GX19-26934X: Neural Representations in Multi-modal and Multi-lingual Modeling</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical
IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH AND LANGUAGE PROCESSING
ISSN
2329-9290
e-ISSN
2329-9304
Volume of the periodical
30
Issue of the periodical within the volume
5
Country of publishing house
US - UNITED STATES
Number of pages
16
Pages from-to
1902-1917
UT code for WoS article
000811572000001
EID of the result in the Scopus database
2-s2.0-85129456463

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