Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/23705
Title: Automatic speech analysis to early detect functional cognitive decline in elderly population
Authors: Ambrosini, Emilia 
Caielli, M. 
Milis, Marios 
Loizou, Christos P. 
Azzolino, Domenico 
Damanti, Sarah 
Bertagnoli, Laura 
Cesari, M. 
Moccia, Sara 
Cid, Manuel 
De Isla, C. Galan 
Salamanca, P. 
Major Field of Science: Engineering and Technology
Field Category: Medical Engineering
Keywords: Brain;Speech recognition;Population statistics;Cognitive functions;Elderly populations;Leave-one-out cross validations;Linear mixed model analysis
Issue Date: 7-Oct-2019
Source: 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2019, 23-27 July, Berlin, Germany
Conference: Annual International Conference of the IEEE Engineering in Medicine and Biology Society 
Abstract: This study aimed at evaluating whether people with a normal cognitive function can be discriminated from subjects with a mild impairment of cognitive function based on a set of acoustic features derived from spontaneous speech. Voice recordings from 90 Italian subjects (age >65 years; group 1: 47 subjects with MMSE>26; group 2: 43 subjects with 20≤ MMSE ≤26) were collected. Voice samples were processed using a MATLAB-based custom software to derive a broad set of known acoustic features. Linear mixed model analyses were performed to select the features able to significantly distinguish between groups. The selected features (% of unvoiced segments, duration of unvoiced segments, % of voice breaks, speech rate, and duration of syllables), alone or in addition to age and years of education, were used to build a learning-based classifier. The leave-one-out cross validation was used for testing and the classifier accuracy was computed. When the voice features were used alone, an overall classification accuracy of 0.73 was achieved. When age and years of education were additionally used, the overall accuracy increased up to 0.80. These performances were lower than the accuracy of 0.86 found in a recent study. However, in that study the classification was based on several tasks, including more cognitive demanding tasks. Our results are encouraging because acoustic features, derived for the first time only from an ecologic continuous speech task, were able to discriminate people with a normal cognitive function from people with a mild cognitive decline. This study poses the basis for the development of a mobile application performing automatic voice analysis on-the-fly during phone calls, which might potentially support the detection of early signs of functional cognitive decline.
URI: https://hdl.handle.net/20.500.14279/23705
ISBN: 978-1-5386-1311-5
DOI: 10.1109/EMBC.2019.8856768
Rights: © IEEE
Attribution-NonCommercial-NoDerivatives 4.0 International
Type: Conference Papers
Affiliation : Information and Bioengineering 
SignalGeneriX Ltd 
Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico 
Universita Politecnica delle Marche 
Junta de Extremadura 
University of Milan 
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation

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