@inproceedings{s-etal-2025-crewx,
title = "{C}rew{X}@{LT}-{EDI}-2025: Transformer-Based {T}amil {ASR} Fine-Tuning with {AVMD} Denoising and {GRU}-{VAD} for Enhanced Transcription Accuracy",
author = "S, Ganesh Sundhar and
N, Hari Krishnan and
D, Arun Prasad T and
V, Shruthikaa and
G, Jyothish Lal",
editor = "Gkirtzou, Katerina and
{\v{Z}}itnik, Slavko and
Gracia, Jorge and
Gromann, Dagmar and
di Buono, Maria Pia and
Monti, Johanna and
Ionov, Maxim",
booktitle = "Proceedings of the 5th Conference on Language, Data and Knowledge: Fifth Workshop on Language Technology for Equality, Diversity, Inclusion",
month = sep,
year = "2025",
address = "Naples, Italy",
publisher = "Unior Press",
url = "https://aclanthology.org/2025.ltedi-1.3/",
pages = "11--16",
ISBN = "978-88-6719-334-9",
abstract = "This research presents an improved Tamil Automatic Speech Recognition (ASR) system designed to enhance accessibility for elderly and transgender populations by addressing unique language challenges. We address the challenges of Tamil ASR{---}including limited high-quality curated datasets, unique phonetic characteristics, and word-merging tendencies{---}through a comprehensive pipeline. Our methodology integrates Adaptive Variational Mode Decomposition (AVMD) for selective noise reduction based on signal characteristics, Silero Voice Activity Detection (VAD) with GRU architecture to eliminate non-speech segments, and fine-tuning of OpenAI{'}s Whisper model optimized for Tamil transcription. The system employs beam search decoding during inference to further improve accuracy. Our approach achieved state-of-the-art performance with a Word Error Rate (WER) of 31.9,winning first place in the LT-EDI 2025 shared task."
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<abstract>This research presents an improved Tamil Automatic Speech Recognition (ASR) system designed to enhance accessibility for elderly and transgender populations by addressing unique language challenges. We address the challenges of Tamil ASR—including limited high-quality curated datasets, unique phonetic characteristics, and word-merging tendencies—through a comprehensive pipeline. Our methodology integrates Adaptive Variational Mode Decomposition (AVMD) for selective noise reduction based on signal characteristics, Silero Voice Activity Detection (VAD) with GRU architecture to eliminate non-speech segments, and fine-tuning of OpenAI’s Whisper model optimized for Tamil transcription. The system employs beam search decoding during inference to further improve accuracy. Our approach achieved state-of-the-art performance with a Word Error Rate (WER) of 31.9,winning first place in the LT-EDI 2025 shared task.</abstract>
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%0 Conference Proceedings
%T CrewX@LT-EDI-2025: Transformer-Based Tamil ASR Fine-Tuning with AVMD Denoising and GRU-VAD for Enhanced Transcription Accuracy
%A S, Ganesh Sundhar
%A N, Hari Krishnan
%A D, Arun Prasad T.
%A V, Shruthikaa
%A G, Jyothish Lal
%Y Gkirtzou, Katerina
%Y Žitnik, Slavko
%Y Gracia, Jorge
%Y Gromann, Dagmar
%Y di Buono, Maria Pia
%Y Monti, Johanna
%Y Ionov, Maxim
%S Proceedings of the 5th Conference on Language, Data and Knowledge: Fifth Workshop on Language Technology for Equality, Diversity, Inclusion
%D 2025
%8 September
%I Unior Press
%C Naples, Italy
%@ 978-88-6719-334-9
%F s-etal-2025-crewx
%X This research presents an improved Tamil Automatic Speech Recognition (ASR) system designed to enhance accessibility for elderly and transgender populations by addressing unique language challenges. We address the challenges of Tamil ASR—including limited high-quality curated datasets, unique phonetic characteristics, and word-merging tendencies—through a comprehensive pipeline. Our methodology integrates Adaptive Variational Mode Decomposition (AVMD) for selective noise reduction based on signal characteristics, Silero Voice Activity Detection (VAD) with GRU architecture to eliminate non-speech segments, and fine-tuning of OpenAI’s Whisper model optimized for Tamil transcription. The system employs beam search decoding during inference to further improve accuracy. Our approach achieved state-of-the-art performance with a Word Error Rate (WER) of 31.9,winning first place in the LT-EDI 2025 shared task.
%U https://aclanthology.org/2025.ltedi-1.3/
%P 11-16
Markdown (Informal)
[CrewX@LT-EDI-2025: Transformer-Based Tamil ASR Fine-Tuning with AVMD Denoising and GRU-VAD for Enhanced Transcription Accuracy](https://aclanthology.org/2025.ltedi-1.3/) (S et al., LTEDI 2025)
ACL
- Ganesh Sundhar S, Hari Krishnan N, Arun Prasad T D, Shruthikaa V, and Jyothish Lal G. 2025. CrewX@LT-EDI-2025: Transformer-Based Tamil ASR Fine-Tuning with AVMD Denoising and GRU-VAD for Enhanced Transcription Accuracy. In Proceedings of the 5th Conference on Language, Data and Knowledge: Fifth Workshop on Language Technology for Equality, Diversity, Inclusion, pages 11–16, Naples, Italy. Unior Press.