@inproceedings{pranesh-etal-2026-serene,
    title = "{SERENE}@{D}ravidian{L}ang{T}ech 2026: Multimodal Approaches for Depression Detection in {D}ravidian Speech: Acoustic, Spectrogram, and Transformer-Based Models",
    author = "Pranesh, TT  and
      K.K.Thamizhmathi  and
      Vigneshwaran, S  and
      B, Bharathi",
    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-1.55/",
    pages = "354--358",
    ISBN = "979-8-89176-401-9",
    abstract = "This paper presents our submission to the De-pression Detection in Dravidian Languagesshared task at DravidianLangTech 2026. Weinvestigate three complementary approachesfor speech-based depression detection in Tamiland Malayalam: (i) acoustic feature engineer-ing using MFCC and prosodic features with aSupport Vector Machine (SVM) classifier, (ii)a convolutional neural network (CNN) trainedon Mel-spectrogram representations, and (iii)a transformer-based model using Whisper-generated transcripts fine-tuned with XLM-RoBERTa. Experimental results show thatacoustic feature-based SVM and spectrogram-based CNN models achieve the strongestperformance on both Tamil and Malayalamdatasets, while the transformer-based approachalso produces competitive results. We furtherdiscuss limitations and future research direc-tions."
}