Detail publikace

Variational Inference for Acoustic Unit Discovery

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

Originální název

Variational Inference for Acoustic Unit Discovery

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

In this article we proposed to train a nonparametric Bayesian model for automatic units discovery within the Variational Bayes framework. Besides simplifying the training scheme, this approach proves to be fast and yields better solution which makes it more suitable for big databases. However, despite the improvement observed, the model still have difficulties with the diversity of speech and tends to learn a large part of unwanted variability. The HMM model for speech segment is convenient but unrealistic and most likely, stronger model will be needed if one wants to achieve accurate automatic units discovery. We plan to extent the present work by using the VB inference with more complex models, as in13, and to gain leverage of Bayesian language models14 to further improve the accuracy of the discovered units.

Klíčová slova

Bayesian non-parametric, Variational Bayes, acoustic unit discovery

Autoři

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

Vydáno

9. 7. 2016

Nakladatel

Elsevier Science

Místo

Yogyakarta

ISSN

1877-0509

Periodikum

Procedia Computer Science

Ročník

2016

Číslo

81

Stát

Nizozemsko

Strany od

80

Strany do

86

Strany počet

7

URL

http://www.sciencedirect.com/science/article/pii/S1877050916300473

BibTex

@inproceedings{BUT131006,
  author="Lucas Antoine Francois {Ondel} and Lukáš {Burget} and Jan {Černocký}",
  title="Variational Inference for Acoustic Unit Discovery",
  booktitle="Procedia Computer Science",
  year="2016",
  journal="Procedia Computer Science",
  volume="2016",
  number="81",
  pages="80--86",
  publisher="Elsevier Science",
  address="Yogyakarta",
  doi="10.1016/j.procs.2016.04.033",
  issn="1877-0509",
  url="http://www.sciencedirect.com/science/article/pii/S1877050916300473"
}