@inproceedings{acharya-etal-2025-junlp,
title = "{JUNLP}@{LT}-{EDI}-2025: Efficient Low-Rank Adaptation of Whisper for Inclusive {T}amil Speech Recognition Targeting Vulnerable Populations",
author = "Acharya, Priyobroto and
Chaudhuri, Soham and
Das, Sayan and
Saha, Dipanjan and
Das, Dipankar",
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-1.4/",
pages = "17--25",
ISBN = "978-88-6719-334-9",
abstract = "Speech recognition has received extensive research attention in recent years. It becomes much more challenging when the speaker{'}s age, gender and other factors introduce variations in the speech. In this work, we propose a fine-tuned automatic speech recognition model derived from OpenAI{'}s whisperlarge-v2. Though we experimented with both Whisper-large and Wav2vec2-XLSR-large, the reduced WER of whisper-large proved to be a superior model. We secured 4th rank in the LT-EDI-2025 shared task. Our implementation details and code are available at our GitHub repository1."
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<abstract>Speech recognition has received extensive research attention in recent years. It becomes much more challenging when the speaker’s age, gender and other factors introduce variations in the speech. In this work, we propose a fine-tuned automatic speech recognition model derived from OpenAI’s whisperlarge-v2. Though we experimented with both Whisper-large and Wav2vec2-XLSR-large, the reduced WER of whisper-large proved to be a superior model. We secured 4th rank in the LT-EDI-2025 shared task. Our implementation details and code are available at our GitHub repository1.</abstract>
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%0 Conference Proceedings
%T JUNLP@LT-EDI-2025: Efficient Low-Rank Adaptation of Whisper for Inclusive Tamil Speech Recognition Targeting Vulnerable Populations
%A Acharya, Priyobroto
%A Chaudhuri, Soham
%A Das, Sayan
%A Saha, Dipanjan
%A Das, Dipankar
%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 acharya-etal-2025-junlp
%X Speech recognition has received extensive research attention in recent years. It becomes much more challenging when the speaker’s age, gender and other factors introduce variations in the speech. In this work, we propose a fine-tuned automatic speech recognition model derived from OpenAI’s whisperlarge-v2. Though we experimented with both Whisper-large and Wav2vec2-XLSR-large, the reduced WER of whisper-large proved to be a superior model. We secured 4th rank in the LT-EDI-2025 shared task. Our implementation details and code are available at our GitHub repository1.
%U https://aclanthology.org/2025.ltedi-1.4/
%P 17-25
Markdown (Informal)
[JUNLP@LT-EDI-2025: Efficient Low-Rank Adaptation of Whisper for Inclusive Tamil Speech Recognition Targeting Vulnerable Populations](https://aclanthology.org/2025.ltedi-1.4/) (Acharya et al., LTEDI 2025)
ACL
- Priyobroto Acharya, Soham Chaudhuri, Sayan Das, Dipanjan Saha, and Dipankar Das. 2025. JUNLP@LT-EDI-2025: Efficient Low-Rank Adaptation of Whisper for Inclusive Tamil Speech Recognition Targeting Vulnerable Populations. In Proceedings of the 5th Conference on Language, Data and Knowledge: Fifth Workshop on Language Technology for Equality, Diversity, Inclusion, pages 17–25, Naples, Italy. Unior Press.