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    <titleInfo>
        <title>Mandarin-English Code-Switching Speech Recognition System for Specific Domain</title>
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    <name type="personal">
        <namePart type="given">Chung-Pu</namePart>
        <namePart type="family">Chiou</namePart>
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        <namePart type="given">Hou-An</namePart>
        <namePart type="family">Lin</namePart>
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    <name type="personal">
        <namePart type="given">Chia-Ping</namePart>
        <namePart type="family">Chen</namePart>
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        <dateIssued>2022-11</dateIssued>
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            <title>Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022)</title>
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            <publisher>The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)</publisher>
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    <abstract>This paper will introduce the use of Automatic Speech Recognition (ASR) technology to process speech content with specific domain. We will use the Conformer end-to-end model as the system architecture, and use pure Chinese data for initial training. Next, use the transfer learning technology to fine-tune the system with Mandarin-English code-switching data. Finally, use the Mandarin-English code-switching data with a specific domain makes the final fine-tuning of the model so that it can achieve a certain effect on speech recognition in a specific domain. Experiments with different fine-tuning methods reduce the final error rate from 82.0% to 34.8%.</abstract>
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%0 Conference Proceedings
%T Mandarin-English Code-Switching Speech Recognition System for Specific Domain
%A Chiou, Chung-Pu
%A Lin, Hou-An
%A Chen, Chia-Ping
%S Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022)
%D 2022
%8 November
%I The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
%C Taipei, Taiwan
%G Chinese
%F chiou-etal-2022-mandarin
%X This paper will introduce the use of Automatic Speech Recognition (ASR) technology to process speech content with specific domain. We will use the Conformer end-to-end model as the system architecture, and use pure Chinese data for initial training. Next, use the transfer learning technology to fine-tune the system with Mandarin-English code-switching data. Finally, use the Mandarin-English code-switching data with a specific domain makes the final fine-tuning of the model so that it can achieve a certain effect on speech recognition in a specific domain. Experiments with different fine-tuning methods reduce the final error rate from 82.0% to 34.8%.
%U https://aclanthology.org/2022.rocling-1.25
%P 200-204

Markdown (Informal)

[Mandarin-English Code-Switching Speech Recognition System for Specific Domain](https://aclanthology.org/2022.rocling-1.25) (Chiou et al., ROCLING 2022)

• Mandarin-English Code-Switching Speech Recognition System for Specific Domain (Chiou et al., ROCLING 2022)

ACL

• Chung-Pu Chiou, Hou-An Lin, and Chia-Ping Chen. 2022. Mandarin-English Code-Switching Speech Recognition System for Specific Domain. In Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022), pages 200–204, Taipei, Taiwan. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP).