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Sensors 2015, 15(11), 28070-28087; doi:10.3390/s151128070

Smartphone Application for the Analysis of Prosodic Features in Running Speech with a Focus on Bipolar Disorders: System Performance Evaluation and Case Study

1
Dipartimento di Ingegneria dell’Informazione, University of Pisa, Via G. Caruso 16, Pisa 56122, Italy
2
Research Center “E. Piaggio”, University of Pisa, Largo L. Lazzarino 1, Pisa 56122, Italy
3
Life Supporting Technologies, Universidad Politécnica de Madrid , Avd. Complutense 30, Madrid 28040, Spain
4
Department of Surgical, Medical, Molecular Pathology and Critical Care, University of Pisa, Via Savi 10, Pisa 56126, Italy
5
Department of General Psychology, University of Padua, Via Venezia 8, Padua 35131, Italy
6
Department of Psychiatry and Mental Health, Strasbourg University Hospital, INSERM U1114, Translational Medicine Federation, University of Strasbourg, Strasbourg 67000, France
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 31 July 2015 / Revised: 26 September 2015 / Accepted: 26 October 2015 / Published: 6 November 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [1214 KB, uploaded 6 November 2015]   |  

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. View Full-Text
Keywords: smartphone application; fundamental frequency; voice segmentation; pitch strength; voice monitoring system; bipolar disorders smartphone application; fundamental frequency; voice segmentation; pitch strength; voice monitoring system; bipolar disorders
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Guidi, A.; Salvi, S.; Ottaviano, M.; Gentili, C.; Bertschy, G.; de Rossi, D.; Scilingo, E.P.; Vanello, N. Smartphone Application for the Analysis of Prosodic Features in Running Speech with a Focus on Bipolar Disorders: System Performance Evaluation and Case Study. Sensors 2015, 15, 28070-28087.

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