@inproceedings{dey-etal-2026-cuet,
    title = "{C}uet{\_}{N}eural{\_}{N}avigators@{D}ravidian{L}ang{T}ech 2026: Depression Detection from {M}alayalam and {T}amil Speech using Self-Supervised Acoustic Models",
    author = "Dey, Shuva  and
      Dey, Abir  and
      Mahmud, Sha Newaz  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://preview.aclanthology.org/ingest-acl-workshops/2026.dravidianlangtech-1.28/",
    pages = "207--211",
    ISBN = "979-8-89176-401-9",
    abstract = "Depression detection from speech aims to findsigns of depression using behavioral signals.This approach enables early mental healthscreening and makes it scalable. However, thetask is tough because of subtle acoustic cues,differences among speakers, and language-specific patterns. In this work, we introduceour system for the Shared Task on DepressionDetection in Dravidian Languages (DD-DL)at DravidianLangTech@ACL 2026. We fo-cus on speech in Tamil and Malayalam. Weexplore pretrained self-supervised speech en-coders, including HuBERT, XLS-R, and Whis-per, to identify acoustic patterns related to de-pression directly from raw audio. Our methodcombines these models through ensembling tocapture different acoustic features. The ex-periments use stratified evaluation and cross-lingual analysis to check how well the mod-els work across languages. Results show thatpretrained acoustic representations effectivelycapture vocal features of depression, achiev-ing Macro-F1 scores of 0.9058 for Tamil and0.9396 for Malayalam. However, cross-lingualtransfer faces challenges because of phoneticand prosodic differences."
}