---
title: "CUET_InferX@DravidianLangTech 2026: Shared Task on Dialect"
description: "The CUET_InferX team describes its systems for the DravidianLangTech@ACL 2026 shared task on… 16 references, cited by 1 paper here."
authors: ["Md. Ashraful Islam Semon", "Jihadul Islam", "Ratnajit Dhar", "Hasan Murad"]
year: 2026
venue: "Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages"
source: "https://aclanthology.org/2026.dravidianlangtech-1.27/"
doi: "10.18653/v1/2026.dravidianlangtech-1.27"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/semon-2026-cuet-inferx-dravidianlangtech-2026-shared/"
---
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# CUET\_InferX@DravidianLangTech 2026: Shared Task on Dialect Based Speech Recognition and Classification in Tamil

**Authors:** Md. Ashraful Islam Semon, Jihadul Islam, [Ratnajit Dhar](https://md.research.elamur.ai/free/en/authors/ratnajit-dhar/) and [Hasan Murad](https://md.research.elamur.ai/free/en/authors/hasan-murad/) · **Year:** 2026 · **Venue:** [Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages](https://md.research.elamur.ai/free/en/venues/dravidianlangtech/) ([Workshop on Speech, Vision, and Language Technologies for Dravidian Languages 2026](https://md.research.elamur.ai/free/en/venues/dravidianlangtech/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.dravidianlangtech-1.27/) · [PDF](https://aclanthology.org/2026.dravidianlangtech-1.27.pdf) · [DOI 10.18653/v1/2026.dravidianlangtech-1.27](https://doi.org/10.18653/v1/2026.dravidianlangtech-1.27) · [OpenAlex](https://openalex.org/W7166834919)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · 1 in our library, of the 149,439 papers whose bibliographies we read

## Summary
The CUET_InferX team describes its systems for the DravidianLangTech@ACL 2026 shared task on Tamil dialects. The task has two parts: transcribing spontaneous dialectal Tamil speech (ASR) and assigning each recording to one of four regions: Northern, Southern, Western or Central. The data come from the multi-dialect Tamil corpus by Bharathi et al. (2025). For ASR, the authors fine-tuned Whisper-large-v3 with LoRA and trained a CTC-based Wav2Vec2 XLSR-53 model whose decoding uses a KenLM 4-gram language model. They merged the two outputs by aligning word sequences and settling disagreements with language-model scores. This combination reached a WER of 0.5410, which placed second. For dialect classification, the team ran a weighted soft-voting ensemble of text transformers over the ASR transcripts. It scored a macro F1 of only 0.2209, placing ninth. Error analysis showed that KenLM tended to replace rare dialect words with standard forms. A later fusion of audio and text features roughly doubled F1. The authors conclude that acoustic cues are essential for identifying Tamil dialects.

## Key points
- **Data split:** training has 5,134 utterances (Southern 1427, Northern 1696, Western 1126, Central 885); test has 579 (212/189/116/62).
- **ASR setup:**
  - Whisper-large-v3 was fine-tuned through Unsloth with LoRA (r = 64, α = 64) on q_proj/v_proj.
  - XLSR-53 was trained with CTC.
  - KenLM decoding used LM weight 0.4, word bonus 1.5 and beam width 500.
  - Outputs were merged via SequenceMatcher alignment, with a slight preference for XLSR.
- **ASR results (WER on test):** XLSR 0.6127, Whisper 0.5510, XLSR+KenLM 0.5491, final ensemble 0.5410 (CER 0.1690).
- **Dialect classification:**
  - The ensemble combined XLM-RoBERTa (weight 1.4), TamilBERT (1.1), IndicBERT and MuRIL (0.9 each).
  - Transcripts from Whisper alone gave better classification than the ensemble's transcripts, because Whisper kept more colloquial forms.
- **Post-competition fusion:** combining Whisper acoustic embeddings with XLM-RoBERTa text embeddings at an early stage raised macro F1 to about 0.40.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Chittagong University of Engineering & Technology](https://md.research.elamur.ai/free/en/universities/chittagong-university-of-engineering-technology/) — University, Chittagong, Bangladesh · 25 works · 29 citations

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🧭 Topics (1): [🎙️ Speech & spoken language](https://md.research.elamur.ai/free/en/topics/speech/) · 480 works

## Cite

Cite as: Md. Ashraful Islam Semon, Jihadul Islam, Ratnajit Dhar and Hasan Murad. 2026. CUET\_InferX@DravidianLangTech 2026: Shared Task on Dialect Based Speech Recognition and Classification in Tamil. Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{semon2026cuet,
  title = {{CUET\_InferX@DravidianLangTech 2026: Shared Task on Dialect Based Speech Recognition and Classification in Tamil}},
  author = {Md. Ashraful Islam Semon, Jihadul Islam, Ratnajit Dhar and Hasan Murad},
  booktitle = {Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages},
  year = {2026},
  doi = {10.18653/v1/2026.dravidianlangtech-1.27},
  url = {https://doi.org/10.18653/v1/2026.dravidianlangtech-1.27}
}
```

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## References

16 references, 5 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 31 October 2026*