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        <title>KT-Speech-Crawler: Automatic Dataset Construction for Speech Recognition from YouTube Videos</title>
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        <namePart type="given">Egor</namePart>
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        <namePart type="given">Sven</namePart>
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    <name type="personal">
        <namePart type="given">Cornelius</namePart>
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    <name type="personal">
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations</title>
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            <namePart type="given">Eduardo</namePart>
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            <namePart type="given">Wei</namePart>
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    <abstract>We describe KT-Speech-Crawler: an approach for automatic dataset construction for speech recognition by crawling YouTube videos. We outline several filtering and post-processing steps, which extract samples that can be used for training end-to-end neural speech recognition systems. In our experiments, we demonstrate that a single-core version of the crawler can obtain around 150 hours of transcribed speech within a day, containing an estimated 3.5% word error rate in the transcriptions. Automatically collected samples contain reading and spontaneous speech recorded in various conditions including background noise and music, distant microphone recordings, and a variety of accents and reverberation. When training a deep neural network on speech recognition, we observed around 40% word error rate reduction on the Wall Street Journal dataset by integrating 200 hours of the collected samples into the training set.</abstract>
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%0 Conference Proceedings
%T KT-Speech-Crawler: Automatic Dataset Construction for Speech Recognition from YouTube Videos
%A Lakomkin, Egor
%A Magg, Sven
%A Weber, Cornelius
%A Wermter, Stefan
%Y Blanco, Eduardo
%Y Lu, Wei
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F lakomkin-etal-2018-kt
%X We describe KT-Speech-Crawler: an approach for automatic dataset construction for speech recognition by crawling YouTube videos. We outline several filtering and post-processing steps, which extract samples that can be used for training end-to-end neural speech recognition systems. In our experiments, we demonstrate that a single-core version of the crawler can obtain around 150 hours of transcribed speech within a day, containing an estimated 3.5% word error rate in the transcriptions. Automatically collected samples contain reading and spontaneous speech recorded in various conditions including background noise and music, distant microphone recordings, and a variety of accents and reverberation. When training a deep neural network on speech recognition, we observed around 40% word error rate reduction on the Wall Street Journal dataset by integrating 200 hours of the collected samples into the training set.
%R 10.18653/v1/D18-2016
%U https://aclanthology.org/D18-2016
%U https://doi.org/10.18653/v1/D18-2016
%P 90-95

Markdown (Informal)

[KT-Speech-Crawler: Automatic Dataset Construction for Speech Recognition from YouTube Videos](https://aclanthology.org/D18-2016) (Lakomkin et al., EMNLP 2018)

• KT-Speech-Crawler: Automatic Dataset Construction for Speech Recognition from YouTube Videos (Lakomkin et al., EMNLP 2018)

ACL

• Egor Lakomkin, Sven Magg, Cornelius Weber, and Stefan Wermter. 2018. KT-Speech-Crawler: Automatic Dataset Construction for Speech Recognition from YouTube Videos. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 90–95, Brussels, Belgium. Association for Computational Linguistics.