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        <title>Honkling: In-Browser Personalization for Ubiquitous Keyword Spotting</title>
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    <name type="personal">
        <namePart type="given">Jaejun</namePart>
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    <name type="personal">
        <namePart type="given">Raphael</namePart>
        <namePart type="family">Tang</namePart>
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    <name type="personal">
        <namePart type="given">Jimmy</namePart>
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            <title>Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations</title>
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        <name type="personal">
            <namePart type="given">Sebastian</namePart>
            <namePart type="family">Padó</namePart>
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        <name type="personal">
            <namePart type="given">Ruihong</namePart>
            <namePart type="family">Huang</namePart>
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    <abstract>Used for simple commands recognition on devices from smart speakers to mobile phones, keyword spotting systems are everywhere. Ubiquitous as well are web applications, which have grown in popularity and complexity over the last decade. However, despite their obvious advantages in natural language interaction, voice-enabled web applications are still few and far between. We attempt to bridge this gap with Honkling, a novel, JavaScript-based keyword spotting system. Purely client-side and cross-device compatible, Honkling can be deployed directly on user devices. Our in-browser implementation enables seamless personalization, which can greatly improve model quality; in the presence of underrepresented, non-American user accents, we can achieve up to an absolute 10% increase in accuracy in the personalized model with only a few examples.</abstract>
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    <identifier type="doi">10.18653/v1/D19-3016</identifier>
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%0 Conference Proceedings
%T Honkling: In-Browser Personalization for Ubiquitous Keyword Spotting
%A Lee, Jaejun
%A Tang, Raphael
%A Lin, Jimmy
%Y Padó, Sebastian
%Y Huang, Ruihong
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F lee-etal-2019-honkling
%X Used for simple commands recognition on devices from smart speakers to mobile phones, keyword spotting systems are everywhere. Ubiquitous as well are web applications, which have grown in popularity and complexity over the last decade. However, despite their obvious advantages in natural language interaction, voice-enabled web applications are still few and far between. We attempt to bridge this gap with Honkling, a novel, JavaScript-based keyword spotting system. Purely client-side and cross-device compatible, Honkling can be deployed directly on user devices. Our in-browser implementation enables seamless personalization, which can greatly improve model quality; in the presence of underrepresented, non-American user accents, we can achieve up to an absolute 10% increase in accuracy in the personalized model with only a few examples.
%R 10.18653/v1/D19-3016
%U https://aclanthology.org/D19-3016
%U https://doi.org/10.18653/v1/D19-3016
%P 91-96

Markdown (Informal)

[Honkling: In-Browser Personalization for Ubiquitous Keyword Spotting](https://aclanthology.org/D19-3016) (Lee et al., EMNLP-IJCNLP 2019)

• Honkling: In-Browser Personalization for Ubiquitous Keyword Spotting (Lee et al., EMNLP-IJCNLP 2019)

ACL

• Jaejun Lee, Raphael Tang, and Jimmy Lin. 2019. Honkling: In-Browser Personalization for Ubiquitous Keyword Spotting. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations, pages 91–96, Hong Kong, China. Association for Computational Linguistics.