<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zayats-ostendorf-2019-giving">
    <titleInfo>
        <title>Giving Attention to the Unexpected: Using Prosody Innovations in Disfluency Detection</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Vicky</namePart>
        <namePart type="family">Zayats</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Mari</namePart>
        <namePart type="family">Ostendorf</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-06</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Jill</namePart>
            <namePart type="family">Burstein</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Christy</namePart>
            <namePart type="family">Doran</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Thamar</namePart>
            <namePart type="family">Solorio</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Minneapolis, Minnesota</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Disfluencies in spontaneous speech are known to be associated with prosodic disruptions. However, most algorithms for disfluency detection use only word transcripts. Integrating prosodic cues has proved difficult because of the many sources of variability affecting the acoustic correlates. This paper introduces a new approach to extracting acoustic-prosodic cues using text-based distributional prediction of acoustic cues to derive vector z-score features (innovations). We explore both early and late fusion techniques for integrating text and prosody, showing gains over a high-accuracy text-only model.</abstract>
    <identifier type="citekey">zayats-ostendorf-2019-giving</identifier>
    <identifier type="doi">10.18653/v1/N19-1008</identifier>
    <location>
        <url>https://aclanthology.org/N19-1008</url>
    </location>
    <part>
        <date>2019-06</date>
        <extent unit="page">
            <start>86</start>
            <end>95</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Giving Attention to the Unexpected: Using Prosody Innovations in Disfluency Detection
%A Zayats, Vicky
%A Ostendorf, Mari
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F zayats-ostendorf-2019-giving
%X Disfluencies in spontaneous speech are known to be associated with prosodic disruptions. However, most algorithms for disfluency detection use only word transcripts. Integrating prosodic cues has proved difficult because of the many sources of variability affecting the acoustic correlates. This paper introduces a new approach to extracting acoustic-prosodic cues using text-based distributional prediction of acoustic cues to derive vector z-score features (innovations). We explore both early and late fusion techniques for integrating text and prosody, showing gains over a high-accuracy text-only model.
%R 10.18653/v1/N19-1008
%U https://aclanthology.org/N19-1008
%U https://doi.org/10.18653/v1/N19-1008
%P 86-95

Markdown (Informal)

[Giving Attention to the Unexpected: Using Prosody Innovations in Disfluency Detection](https://aclanthology.org/N19-1008) (Zayats & Ostendorf, NAACL 2019)

• Giving Attention to the Unexpected: Using Prosody Innovations in Disfluency Detection (Zayats & Ostendorf, NAACL 2019)

ACL

• Vicky Zayats and Mari Ostendorf. 2019. Giving Attention to the Unexpected: Using Prosody Innovations in Disfluency Detection. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 86–95, Minneapolis, Minnesota. Association for Computational Linguistics.