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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" }
Dokumenty
ondel_sltu2016_17-8037.pdf