@inproceedings{eid-etal-2026-trivector,
title = "{T}ri{V}ector@{D}ravidian{L}ang{T}ech 2026: Depression Detection from {T}amil and {M}alayalam Speech with Speaker-Independent Evaluation using {MFCC} and {W}av2{V}ec2",
author = "Eid, Tahmima Hoque and
Tabassum, Fawzia and
Rebayet, Oarisa and
Murad, Hasan",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Thavareesan, Sajeetha and
Rajiakodi, Saranya and
Navaneethakrishnan, Subalalitha and
Chinnappa, Dhivya and
Palani, Balasubramanian and
Subramanian, Malliga and
Shanmugavadivel, Kogilavani and
Rajalakshmi, Ratnavel",
booktitle = "Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for {D}ravidian Languages",
month = jul,
year = "2026",
address = "Underline (Virtual)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.dravidianlangtech-1.68/",
pages = "429--435",
ISBN = "979-8-89176-401-9",
abstract = "Depression is a major mental health concern that can be reflected through subtle changes in speech patterns, prosody, and vocal characteristics. In low-resource and multilingual settings, depression detection from speech may become particularly more challenging. In this work, we present our system for the Shared Task on Depression Detection from Malayalam and Tamil. We explored both handcrafted acoustic features (MFCC) and pretrained speech representations (Wav2Vec2) for depression detection, along with a simple fusion strategy to examine their complementary strengths. Our observations showed that Wav2Vec2 generalized better for Malayalam, whereas for Tamil, a validation-tuned probability fusion performed best. The final system achieved macro-F1 scores of 99.5{\%} for Malayalam and 88.6{\%} for Tamil, securing 3rd place in both tasks."
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%0 Conference Proceedings
%T TriVector@DravidianLangTech 2026: Depression Detection from Tamil and Malayalam Speech with Speaker-Independent Evaluation using MFCC and Wav2Vec2
%A Eid, Tahmima Hoque
%A Tabassum, Fawzia
%A Rebayet, Oarisa
%A Murad, Hasan
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Rajiakodi, Saranya
%Y Navaneethakrishnan, Subalalitha
%Y Chinnappa, Dhivya
%Y Palani, Balasubramanian
%Y Subramanian, Malliga
%Y Shanmugavadivel, Kogilavani
%Y Rajalakshmi, Ratnavel
%S Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2026
%8 July
%I Association for Computational Linguistics
%C Underline (Virtual)
%@ 979-8-89176-401-9
%F eid-etal-2026-trivector
%X Depression is a major mental health concern that can be reflected through subtle changes in speech patterns, prosody, and vocal characteristics. In low-resource and multilingual settings, depression detection from speech may become particularly more challenging. In this work, we present our system for the Shared Task on Depression Detection from Malayalam and Tamil. We explored both handcrafted acoustic features (MFCC) and pretrained speech representations (Wav2Vec2) for depression detection, along with a simple fusion strategy to examine their complementary strengths. Our observations showed that Wav2Vec2 generalized better for Malayalam, whereas for Tamil, a validation-tuned probability fusion performed best. The final system achieved macro-F1 scores of 99.5% for Malayalam and 88.6% for Tamil, securing 3rd place in both tasks.
%U https://aclanthology.org/2026.dravidianlangtech-1.68/
%P 429-435
Markdown (Informal)
[TriVector@DravidianLangTech 2026: Depression Detection from Tamil and Malayalam Speech with Speaker-Independent Evaluation using MFCC and Wav2Vec2](https://aclanthology.org/2026.dravidianlangtech-1.68/) (Eid et al., DravidianLangTech 2026)
ACL