Literature DB >> 3673650

Acoustic analysis of speech variables during depression and after improvement.

A Nilsonne1.   

Abstract

Speech recordings were made of 16 depressed patients during depression and after clinical improvement. The recordings were analyzed using a computer program which extracts acoustic parameters from the fundamental frequency contour of the voice. The percent pause time, the standard deviation of the voice fundamental frequency distribution, the standard deviation of the rate of change of the voice fundamental frequency and the average speed of voice change were found to correlate to the clinical state of the patient. The mean fundamental frequency, the total reading time and the average rate of change of the voice fundamental frequency did not differ between the depressed and the improved group. The acoustic measures were more strongly correlated to the clinical state of the patient as measured by global depression scores than to single depressive symptoms such as retardation or agitation.

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Year:  1987        PMID: 3673650     DOI: 10.1111/j.1600-0447.1987.tb02891.x

Source DB:  PubMed          Journal:  Acta Psychiatr Scand        ISSN: 0001-690X            Impact factor:   6.392


  9 in total

1.  Acoustic analysis in the differentiation of Parkinson's disease and major depression.

Authors:  A J Flint; S E Black; I Campbell-Taylor; G F Gailey; C Levinton
Journal:  J Psycholinguist Res       Date:  1992-09

2.  Remote capture of human voice acoustical data by telephone: a methods study.

Authors:  Michael S Cannizzaro; Nicole Reilly; James C Mundt; Peter J Snyder
Journal:  Clin Linguist Phon       Date:  2005-12       Impact factor: 1.346

3.  Voice acoustic measures of depression severity and treatment response collected via interactive voice response (IVR) technology.

Authors:  James C Mundt; Peter J Snyder; Michael S Cannizzaro; Kara Chappie; Dayna S Geralts
Journal:  J Neurolinguistics       Date:  2007-01       Impact factor: 1.710

4.  The Effect of Talker and Listener Depressive Symptoms on Speech Intelligibility.

Authors:  Hoyoung Yi; Rajka Smiljanic; Bharath Chandrasekaran
Journal:  J Speech Lang Hear Res       Date:  2019-11-18       Impact factor: 2.297

5.  Vocal acoustic biomarkers of depression severity and treatment response.

Authors:  James C Mundt; Adam P Vogel; Douglas E Feltner; William R Lenderking
Journal:  Biol Psychiatry       Date:  2012-04-26       Impact factor: 13.382

Review 6.  Psychomotor retardation in depression: a systematic review of diagnostic, pathophysiologic, and therapeutic implications.

Authors:  Djamila Bennabi; Pierre Vandel; Charalambos Papaxanthis; Thierry Pozzo; Emmanuel Haffen
Journal:  Biomed Res Int       Date:  2013-10-30       Impact factor: 3.411

Review 7.  Voice Stress Analysis: A New Framework for Voice and Effort in Human Performance.

Authors:  Martine Van Puyvelde; Xavier Neyt; Francis McGlone; Nathalie Pattyn
Journal:  Front Psychol       Date:  2018-11-20

Review 8.  Internet of Things for Mental Health: Open Issues in Data Acquisition, Self-Organization, Service Level Agreement, and Identity Management.

Authors:  Leonardo J Gutierrez; Kashif Rabbani; Oluwashina Joseph Ajayi; Samson Kahsay Gebresilassie; Joseph Rafferty; Luis A Castro; Oresti Banos
Journal:  Int J Environ Res Public Health       Date:  2021-02-01       Impact factor: 3.390

Review 9.  Telemonitoring with respect to mood disorders and information and communication technologies: overview and presentation of the PSYCHE project.

Authors:  Hervé Javelot; Anne Spadazzi; Luisa Weiner; Sonia Garcia; Claudio Gentili; Markus Kosel; Gilles Bertschy
Journal:  Biomed Res Int       Date:  2014-06-24       Impact factor: 3.411

  9 in total

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