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    <titleInfo>
        <title>Konkani ASR</title>
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        <namePart type="given">Fadte</namePart>
        <namePart type="family">Swapnil</namePart>
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        <namePart type="family">Gaurish</namePart>
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        <namePart type="given">Pawar</namePart>
        <namePart type="family">Jyoti</namePart>
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        <dateIssued>2023-12</dateIssued>
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            <title>Proceedings of the 20th International Conference on Natural Language Processing (ICON)</title>
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            <namePart type="family">Jyoti</namePart>
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            <namePart type="given">Lalitha</namePart>
            <namePart type="given">Devi</namePart>
            <namePart type="family">Sobha</namePart>
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    <abstract>Konkani is a resource-scarce language, mainly spoken on the west coast of India. The lack of resources directly impacts the development of language technology tools and services. Therefore, the development of digital resources is required to aid in the improvement of this situation. This paper describes the work on the Automatic Speech Recognition (ASR) System for Konkani language. We have created the ASR by fine-tuning the whisper-small ASR model with 100 hours of Konkani speech corpus data. The baseline model showed a word error rate (WER) of 17, which serves as evidence for the efficacy of the fine-tuning procedure in establishing ASR accuracy for Konkani language.</abstract>
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        <url>https://aclanthology.org/2023.icon-1.31</url>
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        <date>2023-12</date>
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            <start>393</start>
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%0 Conference Proceedings
%T Konkani ASR
%A Swapnil, Fadte
%A Gaurish, Thakkar
%A Jyoti, D. Pawar
%Y Jyoti, D. Pawar
%Y Sobha, Lalitha Devi
%S Proceedings of the 20th International Conference on Natural Language Processing (ICON)
%D 2023
%8 December
%I NLP Association of India (NLPAI)
%C Goa University, Goa, India
%F swapnil-etal-2023-konkani
%X Konkani is a resource-scarce language, mainly spoken on the west coast of India. The lack of resources directly impacts the development of language technology tools and services. Therefore, the development of digital resources is required to aid in the improvement of this situation. This paper describes the work on the Automatic Speech Recognition (ASR) System for Konkani language. We have created the ASR by fine-tuning the whisper-small ASR model with 100 hours of Konkani speech corpus data. The baseline model showed a word error rate (WER) of 17, which serves as evidence for the efficacy of the fine-tuning procedure in establishing ASR accuracy for Konkani language.
%U https://aclanthology.org/2023.icon-1.31
%P 393-397

Markdown (Informal)

[Konkani ASR](https://aclanthology.org/2023.icon-1.31) (Swapnil et al., ICON 2023)

• Konkani ASR (Swapnil et al., ICON 2023)

ACL

• Fadte Swapnil, Thakkar Gaurish, and D. Pawar Jyoti. 2023. Konkani ASR. In Proceedings of the 20th International Conference on Natural Language Processing (ICON), pages 393–397, Goa University, Goa, India. NLP Association of India (NLPAI).