@inproceedings{s-etal-2025-nsr,
title = "{NSR}{\_}{LT}-{EDI}-2025 Automatic speech recognition in {T}amil",
author = "S, Nishanth and
Rengarajan, Shruthi and
Rahul, Burugu 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-1.16/",
pages = "95--99",
ISBN = "978-88-6719-334-9",
abstract = "Automatic Speech Recognition (ASR) technology can potentially make marginalized communities more accessible. However, older adultsand transgender speakers are usually highly disadvantaged in accessing valuable services due to low digital literacy and social biases. In Tamil-speaking regions, these are further compounded by the inability of ASR models to address their unique speech types, accents, and spontaneous speaking styles. To bridge this gap, the LT-EDI-2025 shared task is designed to develop robust ASR systems for Tamil speech from vulnerable populations. Using whisper based models, this task is designed to improve recognition rates in speech data collected from older adults and transgender speakers in naturalistic settings such as banks, hospitals and public offices. By bridging the linguistic heterogeneity and acoustic variability among this underrepresented population, the shared task is designed to develop inclusive AI solutions that break communication barriers and empower vulnerable populations in Tamil Nadu."
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<abstract>Automatic Speech Recognition (ASR) technology can potentially make marginalized communities more accessible. However, older adultsand transgender speakers are usually highly disadvantaged in accessing valuable services due to low digital literacy and social biases. In Tamil-speaking regions, these are further compounded by the inability of ASR models to address their unique speech types, accents, and spontaneous speaking styles. To bridge this gap, the LT-EDI-2025 shared task is designed to develop robust ASR systems for Tamil speech from vulnerable populations. Using whisper based models, this task is designed to improve recognition rates in speech data collected from older adults and transgender speakers in naturalistic settings such as banks, hospitals and public offices. By bridging the linguistic heterogeneity and acoustic variability among this underrepresented population, the shared task is designed to develop inclusive AI solutions that break communication barriers and empower vulnerable populations in Tamil Nadu.</abstract>
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%0 Conference Proceedings
%T NSR_LT-EDI-2025 Automatic speech recognition in Tamil
%A S, Nishanth
%A Rengarajan, Shruthi
%A Rahul, Burugu
%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-nsr
%X Automatic Speech Recognition (ASR) technology can potentially make marginalized communities more accessible. However, older adultsand transgender speakers are usually highly disadvantaged in accessing valuable services due to low digital literacy and social biases. In Tamil-speaking regions, these are further compounded by the inability of ASR models to address their unique speech types, accents, and spontaneous speaking styles. To bridge this gap, the LT-EDI-2025 shared task is designed to develop robust ASR systems for Tamil speech from vulnerable populations. Using whisper based models, this task is designed to improve recognition rates in speech data collected from older adults and transgender speakers in naturalistic settings such as banks, hospitals and public offices. By bridging the linguistic heterogeneity and acoustic variability among this underrepresented population, the shared task is designed to develop inclusive AI solutions that break communication barriers and empower vulnerable populations in Tamil Nadu.
%U https://aclanthology.org/2025.ltedi-1.16/
%P 95-99
Markdown (Informal)
[NSR_LT-EDI-2025 Automatic speech recognition in Tamil](https://aclanthology.org/2025.ltedi-1.16/) (S et al., LTEDI 2025)
ACL
- Nishanth S, Shruthi Rengarajan, Burugu Rahul, and Jyothish Lal G. 2025. NSR_LT-EDI-2025 Automatic speech recognition in Tamil. In Proceedings of the 5th Conference on Language, Data and Knowledge: Fifth Workshop on Language Technology for Equality, Diversity, Inclusion, pages 95–99, Naples, Italy. Unior Press.