Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/23705
DC FieldValueLanguage
dc.contributor.authorAmbrosini, Emilia-
dc.contributor.authorCaielli, M.-
dc.contributor.authorMilis, Marios-
dc.contributor.authorLoizou, Christos P.-
dc.contributor.authorAzzolino, Domenico-
dc.contributor.authorDamanti, Sarah-
dc.contributor.authorBertagnoli, Laura-
dc.contributor.authorCesari, M.-
dc.contributor.authorMoccia, Sara-
dc.contributor.authorCid, Manuel-
dc.contributor.authorDe Isla, C. Galan-
dc.contributor.authorSalamanca, P.-
dc.date.accessioned2021-12-10T11:15:35Z-
dc.date.available2021-12-10T11:15:35Z-
dc.date.issued2019-10-07-
dc.identifier.citation41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2019, 23-27 July, Berlin, Germanyen_US
dc.identifier.isbn978-1-5386-1311-5-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/23705-
dc.description.abstractThis 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.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.rights© IEEEen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectBrainen_US
dc.subjectSpeech recognitionen_US
dc.subjectPopulation statisticsen_US
dc.subjectCognitive functionsen_US
dc.subjectElderly populationsen_US
dc.subjectLeave-one-out cross validationsen_US
dc.subjectLinear mixed model analysisen_US
dc.titleAutomatic speech analysis to early detect functional cognitive decline in elderly populationen_US
dc.typeConference Papersen_US
dc.collaborationInformation and Bioengineeringen_US
dc.collaborationSignalGeneriX Ltden_US
dc.collaborationFondazione IRCCS Cà Granda Ospedale Maggiore Policlinicoen_US
dc.collaborationUniversita Politecnica delle Marcheen_US
dc.collaborationJunta de Extremaduraen_US
dc.collaborationUniversity of Milanen_US
dc.subject.categoryMedical Engineeringen_US
dc.countryItalyen_US
dc.countryCyprusen_US
dc.countrySpainen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.relation.conferenceAnnual International Conference of the IEEE Engineering in Medicine and Biology Societyen_US
dc.identifier.doi10.1109/EMBC.2019.8856768en_US
cut.common.academicyear2018-2019en_US
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_c94f-
item.cerifentitytypePublications-
item.openairetypeconferenceObject-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0003-1247-8573-
crisitem.author.parentorgFaculty of Engineering and Technology-
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation
CORE Recommender
Show simple item record

SCOPUSTM   
Citations

15
checked on Nov 6, 2023

Page view(s)

278
Last Week
1
Last month
30
checked on Mar 14, 2025

Google ScholarTM

Check

Altmetric


This item is licensed under a Creative Commons License Creative Commons