2024/09/23 by Sona Binu, Binu, Sona, Fathima Shimna K V +12
Neuroscience · Psychology · #Computation and Language (cs.CL) #Emotion and Mood Recognition #FOS: Computer and information sciences #Mental Health via Writing #Neurobiology of Language and Bilingualism
paper · pdf · doi:10.48550/arxiv.2409.14769
openalex publication_date 2024/09/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The people with Major Depressive Disorder (MDD) exhibit the symptoms of tonal variations in their speech compared to the healthy counterparts. However, these tonal variations not only confine to the state of MDD but also on the language, which has unique tonal patterns. This work analyzes automatic speech-based depression detection across two languages, English and Malayalam, which exhibits distinctive prosodic and phonemic characteristics. We propose an approach that utilizes speech data collected along with self-reported labels from participants reading sentences from IViE corpus, in both English and Malayalam. The IViE corpus consists of five sets of sentences: simple sentences, WH-questions, questions without morphosyntactic markers, inversion questions and coordinations, that can naturally prompt speakers to speak in different tonal patterns. Convolutional Neural Networks (CNNs) are employed for detecting depression from speech. The CNN model is trained to identify acoustic features associated with depression in speech, focusing on both languages. The model's performance is evaluated on the collected dataset containing recordings from both depressed and non-depressed speakers, analyzing its effectiveness in detecting depression across the two languages. Our findings and collected data could contribute to the development of language-agnostic speech-based depression detection systems, thereby enhancing accessibility for diverse populations.