Transfer Learning Based Free-Form Speech Command Classification for Low-Resource Languages
Yohan Karunanayake, Uthayasanker Thayasivam, Surangika Ranathunga
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.- Anthology ID:
- P19-2040
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 288–294
- Language:
- URL:
- https://aclanthology.org/P19-2040
- DOI:
- 10.18653/v1/P19-2040
- Cite (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.
- Cite (Informal):
- Transfer Learning Based Free-Form Speech Command Classification for Low-Resource Languages (Karunanayake et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/P19-2040.pdf