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        <title>Transfer Learning Based Free-Form Speech Command Classification for Low-Resource Languages</title>
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        <namePart type="given">Surangika</namePart>
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    <abstract>Current state-of-the-art speech-based user interfaces use data intense methodologies to recognize free-form speech commands. However, this is not viable for low-resource languages, which lack speech data. This restricts the usability of such interfaces to a limited number of languages. In this paper, we propose a methodology to develop a robust domain-specific speech command classification system for low-resource languages using speech data of a high-resource language. In this transfer learning-based approach, we used a Convolution Neural Network (CNN) to identify a fixed set of intents using an ASR-based character probability map. We were able to achieve significant results for Sinhala and Tamil datasets using an English based ASR, which attests the robustness of the proposed approach.</abstract>
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    <identifier type="doi">10.18653/v1/P19-2040</identifier>
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%0 Conference Proceedings
%T Transfer Learning Based Free-Form Speech Command Classification for Low-Resource Languages
%A Karunanayake, Yohan
%A Thayasivam, Uthayasanker
%A Ranathunga, Surangika
%Y Alva-Manchego, Fernando
%Y Choi, Eunsol
%Y Khashabi, Daniel
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F karunanayake-etal-2019-transfer
%X Current state-of-the-art speech-based user interfaces use data intense methodologies to recognize free-form speech commands. However, this is not viable for low-resource languages, which lack speech data. This restricts the usability of such interfaces to a limited number of languages. In this paper, we propose a methodology to develop a robust domain-specific speech command classification system for low-resource languages using speech data of a high-resource language. In this transfer learning-based approach, we used a Convolution Neural Network (CNN) to identify a fixed set of intents using an ASR-based character probability map. We were able to achieve significant results for Sinhala and Tamil datasets using an English based ASR, which attests the robustness of the proposed approach.
%R 10.18653/v1/P19-2040
%U https://aclanthology.org/P19-2040
%U https://doi.org/10.18653/v1/P19-2040
%P 288-294

Markdown (Informal)

[Transfer Learning Based Free-Form Speech Command Classification for Low-Resource Languages](https://aclanthology.org/P19-2040) (Karunanayake et al., ACL 2019)

• Transfer Learning Based Free-Form Speech Command Classification for Low-Resource Languages (Karunanayake et al., ACL 2019)

ACL

• Yohan Karunanayake, Uthayasanker Thayasivam, and Surangika Ranathunga. 2019. Transfer Learning Based Free-Form Speech Command Classification for Low-Resource Languages. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 288–294, Florence, Italy. Association for Computational Linguistics.