Automatic Conversion of English Pronunciation Using Sequence-to-Sequence Model


KIPS Transactions on Software and Data Engineering, Vol. 6, No. 5, pp. 267-278, May. 2017
10.3745/KTSDE.2017.6.5.267,   PDF Download:
Keywords: Phonetic Alphabet, Word’s Pronunciation, Allophone of English Word
Abstract

As the same letter can be pronounced differently depending on word contexts, one should refer to a lexicon in order to pronounce a word correctly. Phonetic alphabets that lexicons adopt as well as pronunciations that lexicons describe for the same word can be different from lexicon to lexicon. In this paper, we use a sequence-to-sequence model that is widely used in deep learning research area in order to convert automatically from one pronunciation to another. The 12 seq2seq models are implemented based on pronunciation training data collected from 4 different lexicons. The exact accuracy of the models ranges from 74.5% to 89.6%. The aim of this study is the following two things. One is to comprehend a property of phonetic alphabets and pronunciations used in various lexicons. The other is to understand characteristics of seq2seq models by analyzing an error.


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Cite this article
[IEEE Style]
K. J. Lee and Y. S. Choi, "Automatic Conversion of English Pronunciation Using Sequence-to-Sequence Model," KIPS Transactions on Software and Data Engineering, vol. 6, no. 5, pp. 267-278, 2017. DOI: 10.3745/KTSDE.2017.6.5.267.

[ACM Style]
Kong Joo Lee and Yong Seok Choi. 2017. Automatic Conversion of English Pronunciation Using Sequence-to-Sequence Model. KIPS Transactions on Software and Data Engineering, 6, 5, (2017), 267-278. DOI: 10.3745/KTSDE.2017.6.5.267.