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    <titleInfo>
        <title>Automatic Speech Recognition System for Malasar Language using Multilingual Transfer Learning</title>
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    <name type="personal">
        <namePart type="given">Basil</namePart>
        <namePart type="family">K. Raju</namePart>
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    <name type="personal">
        <namePart type="given">Leena</namePart>
        <namePart type="family">G. Pillai</namePart>
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    <name type="personal">
        <namePart type="given">Kavya</namePart>
        <namePart type="family">Manohar</namePart>
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    <name type="personal">
        <namePart type="given">Elizabeth</namePart>
        <namePart type="family">Sherly</namePart>
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            <title>Proceedings of the 20th International Conference on Natural Language Processing (ICON)</title>
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            <namePart type="given">Jyoti</namePart>
            <namePart type="family">D. Pawar</namePart>
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            <namePart type="given">Sobha</namePart>
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    <abstract>This study pioneers the development of an automatic speech recognition (ASR) system for the Malasar language, an extremely low-resource ethnic language spoken by a tribal community in the Western Ghats of South India. Malasar is primarily an oral language which does not have a native script. Therefore, Malasar is often transcribed in Tamil script, a closely related major language. This work presents the first ever effort of leveraging the capabilities of multilingual transfer learning for recognising malasar speech. We fine-tune a pre-trained multilingual transformer model with Malasar speech data. In our endeavour to fine-tune this model using a Malasar speech corpus, we could successfully bring down the WER to 48.00% from 99.08% (zero shot baseline). This work demonstrates the efficacy of multilingual transfer learning in addressing the challenges of ASR for extremely low-resource languages, contributing to the preservation of their linguistic and cultural heritage.</abstract>
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%0 Conference Proceedings
%T Automatic Speech Recognition System for Malasar Language using Multilingual Transfer Learning
%A K. Raju, Basil
%A G. Pillai, Leena
%A Manohar, Kavya
%A Sherly, Elizabeth
%Y D. Pawar, Jyoti
%Y Lalitha Devi, Sobha
%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 basil-etal-2023-automatic
%X This study pioneers the development of an automatic speech recognition (ASR) system for the Malasar language, an extremely low-resource ethnic language spoken by a tribal community in the Western Ghats of South India. Malasar is primarily an oral language which does not have a native script. Therefore, Malasar is often transcribed in Tamil script, a closely related major language. This work presents the first ever effort of leveraging the capabilities of multilingual transfer learning for recognising malasar speech. We fine-tune a pre-trained multilingual transformer model with Malasar speech data. In our endeavour to fine-tune this model using a Malasar speech corpus, we could successfully bring down the WER to 48.00% from 99.08% (zero shot baseline). This work demonstrates the efficacy of multilingual transfer learning in addressing the challenges of ASR for extremely low-resource languages, contributing to the preservation of their linguistic and cultural heritage.
%U https://aclanthology.org/2023.icon-1.41
%P 472-477

Markdown (Informal)

[Automatic Speech Recognition System for Malasar Language using Multilingual Transfer Learning](https://aclanthology.org/2023.icon-1.41) (K. Raju et al., ICON 2023)

• Automatic Speech Recognition System for Malasar Language using Multilingual Transfer Learning (K. Raju et al., ICON 2023)

ACL

• Basil K. Raju, Leena G. Pillai, Kavya Manohar, and Elizabeth Sherly. 2023. Automatic Speech Recognition System for Malasar Language using Multilingual Transfer Learning. In Proceedings of the 20th International Conference on Natural Language Processing (ICON), pages 472–477, Goa University, Goa, India. NLP Association of India (NLPAI).