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        <title>Age Group Classification with Speech and Metadata Multimodality Fusion</title>
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        <namePart type="given">Denys</namePart>
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            <title>Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers</title>
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        <name type="personal">
            <namePart type="given">Mirella</namePart>
            <namePart type="family">Lapata</namePart>
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        <name type="personal">
            <namePart type="given">Phil</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Alexander</namePart>
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    <abstract>Children comprise a significant proportion of TV viewers and it is worthwhile to customize the experience for them. However, identifying who is a child in the audience can be a challenging task. We present initial studies of a novel method which combines utterances with user metadata. In particular, we develop an ensemble of different machine learning techniques on different subsets of data to improve child detection. Our initial results show an 9.2% absolute improvement over the baseline, leading to a state-of-the-art performance.</abstract>
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%0 Conference Proceedings
%T Age Group Classification with Speech and Metadata Multimodality Fusion
%A Katerenchuk, Denys
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F katerenchuk-2017-age
%X Children comprise a significant proportion of TV viewers and it is worthwhile to customize the experience for them. However, identifying who is a child in the audience can be a challenging task. We present initial studies of a novel method which combines utterances with user metadata. In particular, we develop an ensemble of different machine learning techniques on different subsets of data to improve child detection. Our initial results show an 9.2% absolute improvement over the baseline, leading to a state-of-the-art performance.
%U https://aclanthology.org/E17-2030/
%P 188-193

Markdown (Informal)

[Age Group Classification with Speech and Metadata Multimodality Fusion](https://aclanthology.org/E17-2030/) (Katerenchuk, EACL 2017)

• Age Group Classification with Speech and Metadata Multimodality Fusion (Katerenchuk, EACL 2017)

ACL

• Denys Katerenchuk. 2017. Age Group Classification with Speech and Metadata Multimodality Fusion. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 188–193, Valencia, Spain. Association for Computational Linguistics.