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Kesavan, Padmavathi, Lakshmi Travis, Miranda, Aruldoss, Martin and Wynn, Martin G 
ORCID: https://orcid.org/0000-0001-7619-6079
(2026)
Parallel Bilingual Datasets: A Multimodal Deep Learning
Framework for Proficiency and Style Classification.
Multimodal Technologies and Interaction, 10 (5).
pp. 1-27.
doi:10.3390/mti10050047
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16235 Padmavathi et al. (2026) Parallel Bilingual Datasets.pdf - Published Version Available under License Creative Commons Attribution 4.0. Download (1MB) | Preview |
Abstract
This study presents a multimodal deep learning framework for automatic proficiency and style classification of parallel Bilingual Tamil–Hindi learner data. The proposed system employs a dual-headed neural architecture to simultaneously predict proficiency levels (Basic, Advanced) and stylistic categories (Formal, Literary) using shared feature representations. A curated dataset of bilingual text samples is utilized, along with synthetic speech generated through text-to-speech (TTS) to enable controlled multimodal experimentation. Five deep learning architectures are evaluated under text-only, audio-only, and learnable fusion settings. Experimental findings indicate that text-based models consistently achieve strong performance in both proficiency and style classification tasks. In contrast, the audio-only model demonstrates limited effectiveness, highlighting the constraints of synthetic acoustic features in capturing meaningful linguistic information. The fusion models provide only marginal improvements over text-based approaches, suggesting that textual representations play a dominant role in proficiency and stylistic classification within controlled datasets. These results emphasize the importance of linguistic features over acoustic signals for automated language assessment in low-resource settings. The proposed framework provides a scalable and reproducible approach and offers a foundation for future work incorporating real speech data and more diverse linguistic inputs.
| Item Type: | Article |
|---|---|
| Article Type: | Article |
| Uncontrolled Keywords: | Multimodal learning; Language proficiency classification; Style classification; Deep learning; Tamil–Hindi dataset Article Metrics |
| Related URLs: | |
| Subjects: | Q Science > Q Science (General) > Q336 Artificial intelligence Q Science > QA Mathematics > QA76 Computer software > QA76.9 Other topics > QA76.9.A43 Algorithms |
| Divisions: | Schools and Research Institutes > School of Business, Computing and Social Sciences |
| Depositing User: | Martin Wynn |
| Date Deposited: | 06 May 2026 12:38 |
| Last Modified: | 06 May 2026 13:00 |
| URI: | https://eprints.glos.ac.uk/id/eprint/16235 |
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