Result Details

Bayesian Subspace Hidden Markov Model for Acoustic Unit Discovery

ONDEL YANG, L.; VYDANA, H.; BURGET, L.; ČERNOCKÝ, J. Bayesian Subspace Hidden Markov Model for Acoustic Unit Discovery. In Proceedings of Interspeech 2019. Proceedings of Interspeech. Graz: International Speech Communication Association, 2019. no. 9, p. 261-265. ISSN: 1990-9772.
Type
conference paper
Language
English
Authors
Ondel Lucas Antoine Francois, Mgr., Ph.D., DCGM (FIT)
Vydana Hari Krishna, DCGM (FIT)
Burget Lukáš, doc. Ing., Ph.D., DCGM (FIT)
Černocký Jan, prof. Dr. Ing., DCGM (FIT)
Abstract

This work tackles the problem of learning a set of language specificacoustic units from unlabeled speech recordings given aset of labeled recordings from other languages. Our approachmay be described by the following two steps procedure: firstthe model learns the notion of acoustic units from the labelleddata and then the model uses its knowledge to find new acousticunits on the target language. We implement this processwith the Bayesian Subspace Hidden Markov Model (SHMM), amodel akin to the Subspace Gaussian Mixture Model (SGMM)where each low dimensional embedding represents an acousticunit rather than just a HMMs state. The subspace is trainedon 3 languages from the GlobalPhone corpus (German, Polishand Spanish) and the AUs are discovered on the TIMIT corpus.Results, measured in equivalent Phone Error Rate, show thatthis approach significantly outperforms previous HMM basedacoustic units discovery systems and compares favorably withthe Variational Auto Encoder-HMM.

Keywords

Bayesian Inference, Hidden Markov Model,Subspace Model, Variational Bayes, Low-resource languages,Acoustic Unit Discovery

URL
Published
2019
Pages
261–265
Journal
Proceedings of Interspeech, vol. 2019, no. 9, ISSN 1990-9772
Proceedings
Proceedings of Interspeech 2019
Conference
Interspeech Conference
Publisher
International Speech Communication Association
Place
Graz
DOI
10.21437/Interspeech.2019-2224
UT WoS
000831796400053
EID Scopus
2-s2.0-85074724345
BibTeX 
@inproceedings{BUT159991,
  author="Lucas Antoine Francois {Ondel} and Hari Krishna {Vydana} and Lukáš {Burget} and Jan {Černocký}",
  title="Bayesian Subspace Hidden Markov Model for Acoustic Unit Discovery",
  booktitle="Proceedings of Interspeech 2019",
  year="2019",
  journal="Proceedings of Interspeech",
  volume="2019",
  number="9",
  pages="261--265",
  publisher="International Speech Communication Association",
  address="Graz",
  doi="10.21437/Interspeech.2019-2224",
  issn="1990-9772",
  url="https://www.isca-speech.org/archive/Interspeech_2019/pdfs/2224.pdf"
}
Files
Projects
IARPA Machine Translation for English Retrieval of Information in Any Language (MATERIAL) - Foreign Language Automated Information Retrieval (FLAIR), IARPA, start: 2017-09-21, end: 2021-10-22, completed
Information mining in speech acquired by distant microphones, MV, Bezpečnostní výzkum České republiky 2015-2020, VI20152020025, start: 2015-10-01, end: 2020-09-30, completed
IT4Innovations excellence in science, MŠMT, Národní program udržitelnosti II, LQ1602, start: 2016-01-01, end: 2020-12-31, completed
Neural Representations in multi-modal and multi-lingual modeling, GACR, Grantové projekty exelence v základním výzkumu EXPRO - 2019, GX19-26934X, start: 2019-01-01, end: 2023-12-31, completed
Zpracování, zobrazování a analýza multimediálních a 3D dat, BUT, Vnitřní projekty VUT, FIT-S-17-3984, start: 2017-03-01, end: 2020-02-29, completed
Research groups
Speech Data Mining Research Group BUT Speech@FIT (RG SPEECH)
Departments
Department of Computer Graphics and Multimedia (DCGM)
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