Literature DB >> 22269358

[Expressive prosodic patterns in individuals with Alzheimer's disease].

Francisco Martínez-Sánchez1, Juan José García Meilán, Enrique Pérez, Juan Carro, José María Arana.   

Abstract

This paper describes a useful technique for quantifying the degree of speech deficits in dementia of GDS 4 Alzheimer's type (DAT). Production of prosodic speech in DAT patients and healthy older controls was analysed using variation in fundamental frequency (F0) measures on a reading task. The prosogram computational model was used to analyze the prosodic contours of the speech samples, using melodic styling of F0 based on perceptual principles and prominence detection of spectral and amplitude fluctuations in the speech signal. Results revealed significant differences in most of these prosodic parameters among the DAT group. Normal pitch variation in speech and variations in syllable timing were reduced in the DAT group, these features cause "flat" speech prosody in these patients. These speech parameters may have diagnostic and prognostic value for Alzheimer's disease and therefore could be a useful aid in clinical trials.

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Mesh:

Year:  2012        PMID: 22269358

Source DB:  PubMed          Journal:  Psicothema        ISSN: 0214-9915


  7 in total

1.  A novel method for early diagnosis of Alzheimer's disease based on higher-order spectral estimation of spontaneous speech signals.

Authors:  Mahda Nasrolahzadeh; Zeynab Mohammadpoory; Javad Haddadnia
Journal:  Cogn Neurodyn       Date:  2016-09-07       Impact factor: 5.082

2.  Voice biomarkers as indicators of cognitive changes in middle and later adulthood.

Authors:  Elizabeth Mahon; Margie E Lachman
Journal:  Neurobiol Aging       Date:  2022-07-01       Impact factor: 5.133

Review 3.  Emotional and Neuropsychiatric Disorders Associated with Alzheimer's Disease.

Authors:  Kenneth M Heilman; Stephen E Nadeau
Journal:  Neurotherapeutics       Date:  2022-01-10       Impact factor: 6.088

4.  Higher-order spectral analysis of spontaneous speech signals in Alzheimer's disease.

Authors:  Mahda Nasrolahzadeh; Zeynab Mohammadpoory; Javad Haddadnia
Journal:  Cogn Neurodyn       Date:  2018-08-27       Impact factor: 5.082

5.  Automatic speech analysis for the assessment of patients with predementia and Alzheimer's disease.

Authors:  Alexandra König; Aharon Satt; Alexander Sorin; Ron Hoory; Orith Toledo-Ronen; Alexandre Derreumaux; Valeria Manera; Frans Verhey; Pauline Aalten; Phillipe H Robert; Renaud David
Journal:  Alzheimers Dement (Amst)       Date:  2015-03-29

6.  Combining Multimodal Behavioral Data of Gait, Speech, and Drawing for Classification of Alzheimer's Disease and Mild Cognitive Impairment.

Authors:  Yasunori Yamada; Kaoru Shinkawa; Masatomo Kobayashi; Vittorio Caggiano; Miyuki Nemoto; Kiyotaka Nemoto; Tetsuaki Arai
Journal:  J Alzheimers Dis       Date:  2021       Impact factor: 4.472

7.  On the selection of non-invasive methods based on speech analysis oriented to automatic Alzheimer disease diagnosis.

Authors:  Karmele López-de-Ipiña; Jesus-Bernardino Alonso; Carlos Manuel Travieso; Jordi Solé-Casals; Harkaitz Egiraun; Marcos Faundez-Zanuy; Aitzol Ezeiza; Nora Barroso; Miriam Ecay-Torres; Pablo Martinez-Lage; Unai Martinez de Lizardui
Journal:  Sensors (Basel)       Date:  2013-05-21       Impact factor: 3.576

  7 in total

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