Publication detail

Bayesian Subspace Hidden Markov Model for Acoustic Unit Discovery

ONDEL YANG, L. VYDANA, H. BURGET, L. ČERNOCKÝ, J.

Original Title

Bayesian Subspace Hidden Markov Model for Acoustic Unit Discovery

Type

conference paper

Language

English

Original Abstract

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

Keywords

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

Authors

ONDEL YANG, L.; VYDANA, H.; BURGET, L.; ČERNOCKÝ, J.

Released

15. 9. 2019

Publisher

International Speech Communication Association

Location

Graz

ISBN

1990-9772

Periodical

Proceedings of Interspeech

Year of study

2019

Number

9

State

French Republic

Pages from

261

Pages to

265

Pages count

5

URL

https://www.isca-speech.org/archive/Interspeech_2019/pdfs/2224.pdf

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"
}