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            <namePart type="given">Katerina</namePart>
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    <abstract>Speech recognition is a helpful tool for accessing technology and allowing people to interact with technology naturally. This is especially true for people who want to access technology but may encounter challenges interacting with technology in traditional formats. Some examples of these people include the elderly or people from the transgender community. This research presents an Automatic Speech Recognition (ASR) system developed for Tamil-speaking elderly and transgender people who are generally underrepresented in mainstream ASR training datasets. The proposed work used the speech data shared by the task organisers of LT-EDI2025. In the proposed work used the fine-tuned model of OpenAI’s Whisper model with Parameter-Efficient Fine-Tuning (P-EFT) with Low-Rank Adaptation (LoRA) along with SpecAugment, and used the AdamW optimization method. The model’s work led to an overall Word Error Rate (WER) of 42.3% on the untranscribed test data. A key feature of our work is that it demonstrates potential equitable and accessible ASR systems addressing the linguistic and acoustic features of vulnerable groups.</abstract>
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%0 Conference Proceedings
%T SSNCSE@LT-EDI-2025:Speech Recognition for Vulnerable Individuals in Tamil
%A K, Sreeja
%A B, Bharathi
%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 k-b-2025-ssncse-lt-edi
%X Speech recognition is a helpful tool for accessing technology and allowing people to interact with technology naturally. This is especially true for people who want to access technology but may encounter challenges interacting with technology in traditional formats. Some examples of these people include the elderly or people from the transgender community. This research presents an Automatic Speech Recognition (ASR) system developed for Tamil-speaking elderly and transgender people who are generally underrepresented in mainstream ASR training datasets. The proposed work used the speech data shared by the task organisers of LT-EDI2025. In the proposed work used the fine-tuned model of OpenAI’s Whisper model with Parameter-Efficient Fine-Tuning (P-EFT) with Low-Rank Adaptation (LoRA) along with SpecAugment, and used the AdamW optimization method. The model’s work led to an overall Word Error Rate (WER) of 42.3% on the untranscribed test data. A key feature of our work is that it demonstrates potential equitable and accessible ASR systems addressing the linguistic and acoustic features of vulnerable groups.
%U https://aclanthology.org/2025.ltedi-1.2/
%P 6-10

Markdown (Informal)

[SSNCSE@LT-EDI-2025:Speech Recognition for Vulnerable Individuals in Tamil](https://aclanthology.org/2025.ltedi-1.2/) (K & B, LTEDI 2025)

• SSNCSE@LT-EDI-2025:Speech Recognition for Vulnerable Individuals in Tamil (K & B, LTEDI 2025)

ACL

• Sreeja K and Bharathi B. 2025. SSNCSE@LT-EDI-2025:Speech Recognition for Vulnerable Individuals in Tamil. In Proceedings of the 5th Conference on Language, Data and Knowledge: Fifth Workshop on Language Technology for Equality, Diversity, Inclusion, pages 6–10, Naples, Italy. Unior Press.