---
title: "IIITK_SpeechScape@DravidianLangTech 2026: Dialect based"
description: "This shared-task paper from IIIT Kottayam addresses two problems in Tamil speech processing: identifying which… 0.57 CER. 13 references."
authors: ["G Srishtik Sekar", "Harissh Ragav Dhamodaran", "Kishore Shankar S", "Balasubramanian Palani", "R Tharaniya Sairaj"]
year: 2026
venue: "Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages"
source: "https://aclanthology.org/2026.dravidianlangtech-1.40/"
doi: "10.18653/v1/2026.dravidianlangtech-1.40"
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/sekar-2026-iiitk-speechscape-dravidianlangtech-2026-dialect/"
---
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# IIITK\_SpeechScape@DravidianLangTech 2026: Dialect based speech recognition and classification using Speech Foundation Models and Deep Learning Techniques

**Authors:** G Srishtik Sekar, Harissh Ragav Dhamodaran, Kishore Shankar S, [Balasubramanian Palani](https://md.research.elamur.ai/free/en/authors/balasubramanian-palani/) and R Tharaniya Sairaj · **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-1.40/) · [PDF](https://aclanthology.org/2026.dravidianlangtech-1.40.pdf) · [DOI 10.18653/v1/2026.dravidianlangtech-1.40](https://doi.org/10.18653/v1/2026.dravidianlangtech-1.40) · [OpenAlex](https://openalex.org/W7166802502)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
This shared-task paper from IIIT Kottayam addresses two problems in Tamil speech processing: identifying which regional dialect an utterance belongs to, and transcribing dialectal speech into text. Tamil's dialects differ in pronunciation, vocabulary and intonation, which makes both tasks hard for a low-resource language. The authors used a dataset of about 9.4 hours of speech covering four dialects (Central, Southern, Northern, Western). They resampled the audio to 16 kHz mono and discarded recordings below 10 dB SNR.

For dialect classification, they compared a CLDNN baseline (CNN plus BiLSTM over log-Mel features) with three pretrained encoders: Whisper, WavLM and wav2vec. Each encoder used a learnable weighted pooling of its layers, followed by a projection and a feed-forward classifier. For ASR, they fine-tuned Whisper-v3-large with LoRA, optionally adding IndicVoices data, and ran IndicConformer zero-shot as a comparison. Whisper performed best on both tasks. This suggests that large supervised multilingual pretraining transfers well to Tamil dialects, although overall scores remain modest.

## Key points
- **Dialect classification (Macro F1):** Whisper scored 0.46 (accuracy 0.64), ahead of CLDNN (0.33), WavLM (0.30) and Wav2Vec2 (0.26).
- **ASR results:**
  - Whisper fine-tuned on IndicVoices plus dialect data was best (WER 0.57, CER 0.17).
  - Zero-shot IndicConformer came second (WER 0.61, CER 0.20).
  - Whisper fine-tuned on dialect data alone had a worse WER than the untuned base model (0.79 vs 0.76), though its CER was lower (0.25 vs 0.31).
- **Dataset composition:** Northern 3:29 h, Southern 2:44 h, Western 2:00 h, Central 1:08 h, for 9:22 h in total.
- **Per-dialect behavior:** Whisper classified Southern speech best, Northern moderately, and Central and Western poorly. The authors attribute the weak classes to limited training data, although Northern is the largest class yet is not the best recognized.
- **Training setup and availability:** all experiments ran on two NVIDIA T4 GPUs. The code is published on GitHub (GL3MON/IIITK-SpeechScape-DravidianLangTech2026).

*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

- [Indian Institute of Information Technology Kottayam](https://md.research.elamur.ai/free/en/universities/indian-institute-of-information-technology-kottayam/) — University, Palai, India · 2 works · 1 citation

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## Cite

Cite as: G Srishtik Sekar, Harissh Ragav Dhamodaran, Kishore Shankar S, Balasubramanian Palani and R Tharaniya Sairaj. 2026. IIITK\_SpeechScape@DravidianLangTech 2026: Dialect based speech recognition and classification using Speech Foundation Models and Deep Learning Techniques. 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{sekar2026iiitk,
  title = {{IIITK\_SpeechScape@DravidianLangTech 2026: Dialect based speech recognition and classification using Speech Foundation Models and Deep Learning Techniques}},
  author = {G Srishtik Sekar, Harissh Ragav Dhamodaran, Kishore Shankar S, Balasubramanian Palani and R Tharaniya Sairaj},
  booktitle = {Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages},
  year = {2026},
  doi = {10.18653/v1/2026.dravidianlangtech-1.40},
  url = {https://doi.org/10.18653/v1/2026.dravidianlangtech-1.40}
}
```

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## References

13 references, 3 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 1 November 2026*