Influence of speaker de‐identification in depression detection
Author:
Affiliation:
1. AtlantTIC Research CenterUniversity of VigoE.E. Telecomunicación, Campus Universitario s/n, 36310 VigoSpain
2. Ikerbasque, Basque Foundation for ScienceUniversity of the Basque CountryAlda, Urquijo s/n, 48013 BilbaoSpain
Funder
Secretaría de Estado de Investigación, Desarrollo e Innovación
Publisher
Institution of Engineering and Technology (IET)
Subject
Electrical and Electronic Engineering,Signal Processing
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-spr.2016.0731
Reference35 articles.
1. World Health Organization: ‘Depression: a global public health concern’ 2012
2. Current approaches to assessment and treatment of persons with serious mental illness.
3. Review: Suicide and schizophrenia: a systematic review of rates and risk factors
4. Cummins N.: ‘Automatic assessment of depression from speech: Paralinguistic analysis modelling and machine learning’. PhD thesis The University of New South Wales 2016
5. Karam Z.N. Provost E.M. Singh S.et al: ‘Ecologically valid long‐term mood monitoring of individuals with bipolar disorder using speech’.Proc. ICASSP 2014 pp.4858–4862
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1. Depression detection based on linear and nonlinear speech features in I-vector/SVDA framework;Computers in Biology and Medicine;2022-10
2. Analysis of gender and identity issues in depression detection on de-identified speech;Computer Speech & Language;2021-01
3. Analysis of the characteristics of English part of speech based on unsupervised machine learning and image recognition model;Journal of Intelligent & Fuzzy Systems;2020-08-31
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