<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="kyaw-thu-etal-2016-comparison">
    <titleInfo>
        <title>Comparison of Grapheme-to-Phoneme Conversion Methods on a Myanmar Pronunciation Dictionary</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Ye</namePart>
        <namePart type="family">Kyaw Thu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Win</namePart>
        <namePart type="family">Pa Pa</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yoshinori</namePart>
        <namePart type="family">Sagisaka</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Naoto</namePart>
        <namePart type="family">Iwahashi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2016-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 6th Workshop on South and Southeast Asian Natural Language Processing (WSSANLP2016)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Dekai</namePart>
            <namePart type="family">Wu</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Pushpak</namePart>
            <namePart type="family">Bhattacharyya</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>The COLING 2016 Organizing Committee</publisher>
            <place>
                <placeTerm type="text">Osaka, Japan</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Grapheme-to-Phoneme (G2P) conversion is the task of predicting the pronunciation of a word given its graphemic or written form. It is a highly important part of both automatic speech recognition (ASR) and text-to-speech (TTS) systems. In this paper, we evaluate seven G2P conversion approaches: Adaptive Regularization of Weight Vectors (AROW) based structured learning (S-AROW), Conditional Random Field (CRF), Joint-sequence models (JSM), phrase-based statistical machine translation (PBSMT), Recurrent Neural Network (RNN), Support Vector Machine (SVM) based point-wise classification, Weighted Finite-state Transducers (WFST) on a manually tagged Myanmar phoneme dictionary. The G2P bootstrapping experimental results were measured with both automatic phoneme error rate (PER) calculation and also manual checking in terms of voiced/unvoiced, tones, consonant and vowel errors. The result shows that CRF, PBSMT and WFST approaches are the best performing methods for G2P conversion on Myanmar language.</abstract>
    <identifier type="citekey">kyaw-thu-etal-2016-comparison</identifier>
    <location>
        <url>https://aclanthology.org/W16-3702</url>
    </location>
    <part>
        <date>2016-12</date>
        <extent unit="page">
            <start>11</start>
            <end>22</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Comparison of Grapheme-to-Phoneme Conversion Methods on a Myanmar Pronunciation Dictionary
%A Kyaw Thu, Ye
%A Pa Pa, Win
%A Sagisaka, Yoshinori
%A Iwahashi, Naoto
%Y Wu, Dekai
%Y Bhattacharyya, Pushpak
%S Proceedings of the 6th Workshop on South and Southeast Asian Natural Language Processing (WSSANLP2016)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F kyaw-thu-etal-2016-comparison
%X Grapheme-to-Phoneme (G2P) conversion is the task of predicting the pronunciation of a word given its graphemic or written form. It is a highly important part of both automatic speech recognition (ASR) and text-to-speech (TTS) systems. In this paper, we evaluate seven G2P conversion approaches: Adaptive Regularization of Weight Vectors (AROW) based structured learning (S-AROW), Conditional Random Field (CRF), Joint-sequence models (JSM), phrase-based statistical machine translation (PBSMT), Recurrent Neural Network (RNN), Support Vector Machine (SVM) based point-wise classification, Weighted Finite-state Transducers (WFST) on a manually tagged Myanmar phoneme dictionary. The G2P bootstrapping experimental results were measured with both automatic phoneme error rate (PER) calculation and also manual checking in terms of voiced/unvoiced, tones, consonant and vowel errors. The result shows that CRF, PBSMT and WFST approaches are the best performing methods for G2P conversion on Myanmar language.
%U https://aclanthology.org/W16-3702
%P 11-22

Markdown (Informal)

[Comparison of Grapheme-to-Phoneme Conversion Methods on a Myanmar Pronunciation Dictionary](https://aclanthology.org/W16-3702) (Kyaw Thu et al., WSSANLP 2016)

• Comparison of Grapheme-to-Phoneme Conversion Methods on a Myanmar Pronunciation Dictionary (Kyaw Thu et al., WSSANLP 2016)

ACL

• Ye Kyaw Thu, Win Pa Pa, Yoshinori Sagisaka, and Naoto Iwahashi. 2016. Comparison of Grapheme-to-Phoneme Conversion Methods on a Myanmar Pronunciation Dictionary. In Proceedings of the 6th Workshop on South and Southeast Asian Natural Language Processing (WSSANLP2016), pages 11–22, Osaka, Japan. The COLING 2016 Organizing Committee.