title: Smartphone Application for the Analysis of Prosodic Features in Running Speech with a Focus on Bipolar Disorders: System Performance Evaluation and Case Study.
abstract: Bipolar disorder is one of the most common mood disorders characterized by large and invalidating mood swings. Several projects focus on the development of decision support systems that monitor and advise patients, as well as clinicians. Voice monitoring and speech signal analysis can be exploited to reach this goal. In this study, an Android application was designed for analyzing running speech using a smartphone device. The application can record audio samples and estimate speech fundamental frequency, F0, and its changes. F0-related features are estimated locally on the smartphone, with some advantages with respect to remote processing approaches in terms of privacy protection and reduced upload costs. The raw features can be sent to a central server and further processed. The quality of the audio recordings, algorithm reliability and performance of the overall system were evaluated in terms of voiced segment detection and features estimation. The results demonstrate that mean F0 from each voiced segment can be reliably estimated, thus describing prosodic features across the speech sample. Instead, features related to F0 variability within each voiced segment performed poorly. A case study performed on a bipolar patient is presented.
authors.affiliation: Dipartimento di Ingegneria dell'Informazione, University of Pisa, Via G. Caruso 16, Pisa 56122, Italy. andrea.guidi@for.unipi.it.|Research Center "E. Piaggio", University of Pisa, Largo L. Lazzarino 1, Pisa 56122, Italy. andrea.guidi@for.unipi.it.
authors.forename: Andrea
authors.initials: A
authors.lastname: Guidi
authors.affiliation: Life Supporting Technologies, Universidad Politécnica de Madrid , Avd. Complutense 30, Madrid 28040, Spain. ssalvi@lst.tfo.upm.es.
authors.forename: Sergio
authors.initials: S
authors.lastname: Salvi
authors.affiliation: Life Supporting Technologies, Universidad Politécnica de Madrid , Avd. Complutense 30, Madrid 28040, Spain. mottaviano@lst.tfo.upm.es.
authors.forename: Manuel
authors.initials: M
authors.lastname: Ottaviano
authors.affiliation: Department of Surgical, Medical, Molecular Pathology and Critical Care, University of Pisa, Via Savi 10, Pisa 56126, Italy. c.gentili@unipd.it.|Department of General Psychology, University of Padua, Via Venezia 8, Padua 35131, Italy. c.gentili@unipd.it.
authors.forename: Claudio
authors.initials: C
authors.lastname: Gentili
authors.affiliation: Department of Psychiatry and Mental Health, Strasbourg University Hospital, INSERM U1114, Translational Medicine Federation, University of Strasbourg, Strasbourg 67000, France. gilles.bertschy@chru-strasbourg.fr.
authors.forename: Gilles
authors.initials: G
authors.lastname: Bertschy
authors.affiliation: Dipartimento di Ingegneria dell'Informazione, University of Pisa, Via G. Caruso 16, Pisa 56122, Italy. d.derossi@centropiaggio.unipi.it.|Research Center "E. Piaggio", University of Pisa, Largo L. Lazzarino 1, Pisa 56122, Italy. d.derossi@centropiaggio.unipi.it.
authors.forename: Danilo
authors.initials: D
authors.lastname: de Rossi
authors.affiliation: Dipartimento di Ingegneria dell'Informazione, University of Pisa, Via G. Caruso 16, Pisa 56122, Italy. e.scilingo@centropiaggio.unipi.it.|Research Center "E. Piaggio", University of Pisa, Largo L. Lazzarino 1, Pisa 56122, Italy. e.scilingo@centropiaggio.unipi.it.
authors.forename: Enzo Pasquale
authors.initials: EP
authors.lastname: Scilingo
authors.affiliation: Dipartimento di Ingegneria dell'Informazione, University of Pisa, Via G. Caruso 16, Pisa 56122, Italy. nicola.vanello@unipi.it.|Research Center "E. Piaggio", University of Pisa, Largo L. Lazzarino 1, Pisa 56122, Italy. nicola.vanello@unipi.it.
authors.forename: Nicola
authors.initials: N
authors.lastname: Vanello
mesh_terms: D000328:Adult; D001714:Bipolar Disorder / Q000503:physiopathology*; D005260:Female; D006801:Humans; D008297:Male; D063731:Mobile Applications; D008991:Monitoring, Physiologic / Q000295:instrumentation* / Q000379:methods; D010865:Pilot Projects; D000068997:Smartphone; D013060:Speech / Q000502:physiology*; D014831:Voice / Q000502:physiology*
keywords: bipolar disorders; fundamental frequency; pitch strength; smartphone application; voice monitoring system; voice segmentation