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Phonetisaurus: Exploring grapheme-to-phoneme conversion with joint n-gram models in the WFST framework

Published online by Cambridge University Press:  07 September 2015

JOSEF ROBERT NOVAK ,
NOBUAKI MINEMATSU  and
KEIKICHI HIROSE
JOSEF ROBERT NOVAK
Affiliation:
The University of Tokyo, Graduate School of Information Science and Technology, Tokyo, Japan e-mails: novakj@gavo.t.u-tokyo.ac.jp, mine@gavo.t.u-tokyo.ac.jp, hirose@gavo.t.u-tokyo.ac.jp
NOBUAKI MINEMATSU
Affiliation:
The University of Tokyo, Graduate School of Information Science and Technology, Tokyo, Japan e-mails: novakj@gavo.t.u-tokyo.ac.jp, mine@gavo.t.u-tokyo.ac.jp, hirose@gavo.t.u-tokyo.ac.jp
KEIKICHI HIROSE
Affiliation:
The University of Tokyo, Graduate School of Information Science and Technology, Tokyo, Japan e-mails: novakj@gavo.t.u-tokyo.ac.jp, mine@gavo.t.u-tokyo.ac.jp, hirose@gavo.t.u-tokyo.ac.jp
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Abstract

This paper provides an analysis of several practical issues related to the theory and implementation of Grapheme-to-Phoneme (G2P) conversion systems utilizing the Weighted Finite-State Transducer paradigm. The paper addresses issues related to system accuracy, training time and practical implementation. The focus is on joint n-gram models which have proven to provide an excellent trade-off between system accuracy and training complexity. The paper argues in favor of simple, productive approaches to G2P, which favor a balance between training time, accuracy and model complexity. The paper also introduces the first instance of using joint sequence RnnLMs directly for G2P conversion, and achieves new state-of-the-art performance via ensemble methods combining RnnLMs and n-gram based models. In addition to detailed descriptions of the approach, minor yet novel implementation solutions, and experimental results, the paper introduces Phonetisaurus, a fully-functional, flexible, open-source, BSD-licensed G2P conversion toolkit, which leverages the OpenFst library. The work is intended to be accessible to a broad range of readers.

Type
Articles
Information
Natural Language Engineering , Volume 22 , Issue 6 , November 2016 , pp. 907 - 938
Copyright
Copyright © Cambridge University Press 2015 

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