Phonetisaurus: Exploring grapheme-to-phoneme conversion with joint n-gram models in the WFST framework. (7th September 2015)

• Record Type: Journal Article
• Authors: NOVAK, JOSEF ROBERT; MINEMATSU, NOBUAKI; HIROSE, KEIKICHI
• Is Part Of: Natural language engineering. Volume 22:Part 6(2016)
• Page Start: 907
• Page End: 938
• Publication Date: 2015-09-07
• Subjects: Natural language processing (Computer science) -- Periodicals; Software engineering -- Periodicals; 006.35
• DOI: 10.1017/S1351324915000315
• Languages: English
• ISSNs: 1351-3249

Abstract

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.