@inproceedings{shanmugavadivel-etal-2025-kec-ai-brightred,
title = "{KEC}{\_}{AI}{\_}{BRIGHTRED}@{D}ravidian{L}ang{T}ech 2025: Multimodal Hate Speech Detection in {D}ravidian languages",
author = "Shanmugavadivel, Kogilavani and
Subramanian, Malliga and
P, Nishdharani and
E, Santhiya and
E, Yaswanth Raj",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Thavareesan, Sajeetha and
Sherly, Elizabeth and
Rajiakodi, Saranya and
Palani, Balasubramanian and
Subramanian, Malliga and
Cn, Subalalitha and
Chinnappa, Dhivya",
booktitle = "Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
month = may,
year = "2025",
address = "Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.dravidianlangtech-1.127/",
doi = "10.18653/v1/2025.dravidianlangtech-1.127",
pages = "754--758",
ISBN = "979-8-89176-228-2",
abstract = {Hate speech detection in multilingual settings presents significant challenges due to linguistic variations and speech patterns across different languages. This study proposes a fusion-based approach that integrates audio and text features to enhance classification accuracy in Tamil, Telugu, and Malayalam. We extract Mel- Frequency Cepstral Coefficients and their delta variations for speech representation, while textbased features contribute additional linguistic insights. Several models were evaluated, including BiLSTM, Capsule Networks with Attention, Capsule-GRU, ConvLSTM-BiLSTM, and Multinomial Na{\"i}ve Bayes, to determine the most effective architecture. Experimental results demonstrate that Random Forest performs best for text classification, while CNN achieves the highest accuracy for audio classification. The model was evaluated using the Macro F1 score and ranked ninth in Tamil with a score of 0.3018, ninth in Telugu with a score of 0.251, and thirteenth in Malayalam with a score of 0.2782 in the Multimodal Social Media Data Analysis in Dravidian Languages shared task at DravidianLangTech@NAACL 2025. By leveraging feature fusion and optimized model selection, this approach provides a scalable and effective framework for multilingual hate speech detection, contributing to improved content moderation on social media platforms.}
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%0 Conference Proceedings
%T KEC_AI_BRIGHTRED@DravidianLangTech 2025: Multimodal Hate Speech Detection in Dravidian languages
%A Shanmugavadivel, Kogilavani
%A Subramanian, Malliga
%A P, Nishdharani
%A E, Santhiya
%A E, Yaswanth Raj
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%Y Rajiakodi, Saranya
%Y Palani, Balasubramanian
%Y Subramanian, Malliga
%Y Cn, Subalalitha
%Y Chinnappa, Dhivya
%S Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2025
%8 May
%I Association for Computational Linguistics
%C Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico
%@ 979-8-89176-228-2
%F shanmugavadivel-etal-2025-kec-ai-brightred
%X Hate speech detection in multilingual settings presents significant challenges due to linguistic variations and speech patterns across different languages. This study proposes a fusion-based approach that integrates audio and text features to enhance classification accuracy in Tamil, Telugu, and Malayalam. We extract Mel- Frequency Cepstral Coefficients and their delta variations for speech representation, while textbased features contribute additional linguistic insights. Several models were evaluated, including BiLSTM, Capsule Networks with Attention, Capsule-GRU, ConvLSTM-BiLSTM, and Multinomial Naïve Bayes, to determine the most effective architecture. Experimental results demonstrate that Random Forest performs best for text classification, while CNN achieves the highest accuracy for audio classification. The model was evaluated using the Macro F1 score and ranked ninth in Tamil with a score of 0.3018, ninth in Telugu with a score of 0.251, and thirteenth in Malayalam with a score of 0.2782 in the Multimodal Social Media Data Analysis in Dravidian Languages shared task at DravidianLangTech@NAACL 2025. By leveraging feature fusion and optimized model selection, this approach provides a scalable and effective framework for multilingual hate speech detection, contributing to improved content moderation on social media platforms.
%R 10.18653/v1/2025.dravidianlangtech-1.127
%U https://aclanthology.org/2025.dravidianlangtech-1.127/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.127
%P 754-758
Markdown (Informal)
[KEC_AI_BRIGHTRED@DravidianLangTech 2025: Multimodal Hate Speech Detection in Dravidian languages](https://aclanthology.org/2025.dravidianlangtech-1.127/) (Shanmugavadivel et al., DravidianLangTech 2025)
ACL
- Kogilavani Shanmugavadivel, Malliga Subramanian, Nishdharani P, Santhiya E, and Yaswanth Raj E. 2025. KEC_AI_BRIGHTRED@DravidianLangTech 2025: Multimodal Hate Speech Detection in Dravidian languages. In Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 754–758, Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico. Association for Computational Linguistics.