Estimating Depressive Symptom Class from Voice

Voice-based depression detection methods have been studied worldwide as an objective and easy method to detect depression. Conventional studies estimate the presence or severity of depression. However, an estimation of symptoms is a necessary technique not only to treat depression, but also to relie...

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Detalles Bibliográficos
Autores principales: Takano, Takeshi, Mizuguchi, Daisuke, Omiya, Yasuhiro, Higuchi, Masakazu, Nakamura, Mitsuteru, Shinohara, Shuji, Mitsuyoshi, Shunji, Saito, Taku, Yoshino, Aihide, Toda, Hiroyuki, Tokuno, Shinichi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10002315/
https://www.ncbi.nlm.nih.gov/pubmed/36900976
http://dx.doi.org/10.3390/ijerph20053965
Descripción
Sumario:Voice-based depression detection methods have been studied worldwide as an objective and easy method to detect depression. Conventional studies estimate the presence or severity of depression. However, an estimation of symptoms is a necessary technique not only to treat depression, but also to relieve patients’ distress. Hence, we studied a method for clustering symptoms from HAM-D scores of depressed patients and by estimating patients in different symptom groups based on acoustic features of their speech. We could separate different symptom groups with an accuracy of 79%. The results suggest that voice from speech can estimate the symptoms associated with depression.

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