Abstract
Smartphone technology is continuously being updated through software and hardware changes. At present, a limited number of studies have been undertaken to assess the impact of these changes on data collection for linguistic research. This paper discusses the potential of smartphones to gather reliable recordings, along with ethical considerations for storing additional personal information when working in other contexts (i.e. healthcare settings). A pilot study was undertaken using the FitvoiceTM account-based application to analyse articulatory proficiency in depressed and healthy participants. Results suggest that phonetic differences exist between these groups in terms of plosive production, and that smartphones are capable of adequately recording these minute aspects of the speech signal for analysis.
References
Adams, Zachary W., Erin A. McClure, Kevin M. Gray, Carla Kmett Danielson, Frank A. Treiber & Kenneth J. Ruggiero. 2017. Mobile devices for the remote acquisition of physiological and behavioral biomarkers in psychiatric clinical research. Journal of Psychiatric Research 85. 1–14. doi:10.1016/j.jpsychires.2016.10.019.10.1016/j.jpsychires.2016.10.019Search in Google Scholar
Alpert, Murray, Enrique R. Pouget & Raul R. Silva. 2001. Reflections of depression in acoustic measures of the patient’s speech. Journal of Affective Disorders 66(1). 59–69. doi:10.1016/S0165-0327(00)00335-9.10.1016/S0165-0327(00)00335-9Search in Google Scholar
Arora, Shifali, Jennifer Yttri & Wendy Nilsen. 2014. Privacy and security in mobile health (mHealth) research. Alcohol Research: Current Reviews 36(1). 143.Search in Google Scholar
Boersma, Paul & David Weenink. 2019. Praat: Doing phonetics by computer. http://www.praat.org.Search in Google Scholar
Brown, A. & G. J. Docherty. 1995. Phonetic variation in dysarthric speech as a function of sampling task. European Journal of Disorders of Communication 30(1). 17–35.10.3109/13682829509031320Search in Google Scholar
Cannizzaro, Michael, Brian Harel, Nicole Reilly, Phillip Chappell & Peter J. Snyder. 2004. Voice acoustical measurement of the severity of major depression. Brain and Cognition 56(1). 30–35. doi:10.1016/j.bandc.2004.05.003.10.1016/j.bandc.2004.05.003Search in Google Scholar
Christopher, Gary & John MacDonald. 2005. The impact of clinical depression on working memory. Cognitive Neuropsychiatry 10(5). 379–399. doi:10.1080/13546800444000128.10.1080/13546800444000128Search in Google Scholar
Cummins, Nicholas, Vidhyasaharan Sethu, Julien Epps, Sebastian Schnieder & Jarek Krajewski. 2015. Analysis of acoustic space variability in speech affected by depression. Speech Communication 75. 27–49. doi:10.1016/j.specom.2015.09.003.10.1016/j.specom.2015.09.003Search in Google Scholar
Decoster, Wivine & Frans Debruyne. 2000. Longitudinal voice changes: Facts and interpretation. Journal of Voice 14(2). 184–193. doi:10.1016/S0892-1997(00)80026-0.10.1016/S0892-1997(00)80026-0Search in Google Scholar
Depp, Colin A., Brent Mausbach, Eric Granholm, Veronica Cardenas, Dror Ben-Zeev, Thomas L. Patterson, Barry D. Lebowitz & Dilip V. Jeste. 2010. Mobile interventions for severe mental illness: Design and preliminary data from three approaches. The Journal of Nervous and Mental Disease 198(10). 715–721. doi:10.1097/NMD.0b013e3181f49ea3.10.1097/NMD.0b013e3181f49ea3Search in Google Scholar
Dogan, Ezgi, Christian Sander, Xenija Wagner, Ulrich Hegerl & Elisabeth Kohls. 2017. Smartphone-Based monitoring of objective and subjective data in affective disorders: Where are we and where are we going? Systematic Review. Journal of Medical Internet Research 19(7). e262. doi:10.2196/jmir.7006.10.2196/jmir.7006Search in Google Scholar
Donker, Tara, Katherine Petrie, Judy Proudfoot, Janine Clarke, Mary-Rose Birch & Helen Christensen. 2013. Smartphones for smarter delivery of mental health programs: A systematic review. Journal of Medical Internet Research 15(11). e247. doi:10.2196/jmir.2791.10.2196/jmir.2791Search in Google Scholar
Flint, Alistair J., Sandra E. Black, Irene Campbell-Taylor, Gillian F. Gailey & Carey Levinton. 1993. Abnormal speech articulation, psychomotor retardation, and subcortical dysfunction in major depression. Journal of Psychiatric Research 27(3). 309–319. doi:10.1016/0022-3956(93)90041-Y.10.1016/0022-3956(93)90041-YSearch in Google Scholar
Grillo, Elizabeth U., Jenna N. Brosious, Staci L. Sorrell & Supraja Anand. 2016. Influence of smartphones and software on acoustic voice measures. International Journal of Telerehabilitation 8(2). 9–14. doi:10.5195/IJT.2016.6202.10.5195/IJT.2016.6202Search in Google Scholar
Hamilton, Max. 1960. A rating scale for depression. Journal of Neurology, Neurosurgery, and Psychiatry 23(1). 56–62.10.1136/jnnp.23.1.56Search in Google Scholar
Heman-Ackah, Y. D., R. J. Heuer, D. D. Michael, R. Ostrowski, M. Horman, M. M. Baroody, J. Hillenbrand & R. T. Sataloff. 2003. Cepstral peak prominence: A more reliable measure of dysphonia. Annals of Otology Rhinology and Laryngology 112(4). 324–333.10.1177/000348940311200406Search in Google Scholar
Honig, Florian, Anton Batliner, Elmar Noth, Sebastian Schnieder & Jarek Krajewski. 2014, Sept 14–18. Automatic modelling of depressed speech: Relevant features and relevance of gender. Interspeech 2014 Conference. Singapore.10.21437/Interspeech.2014-313Search in Google Scholar
Houtkoop, Bobby Lee, Chris Chambers, Malcolm Macleod, Dorothy V. M. Bishop, Thomas E. Nichols & Eric-Jan Wagenmakers. 2018. Data sharing in psychology: A survey on barriers and preconditions. Advances in Methods and Practices in Psychological Science 1(1). 70–85. doi:10.1177/2515245917751886.10.1177/2515245917751886Search in Google Scholar
Jannetts, Stephen, Felix Schaeffler, Janet Beck & Cowen, Steve. 2019. Assessing voice health using smartphones: Bias and random error of acoustic voice parameters captured by different smartphone types. International Journal of Language & Communication Disorders.10.1111/1460-6984.12457Search in Google Scholar
Kardous, Chucri A. & Peter B. Shaw. 2014. Evaluation of smartphone sound measurement applications. The Journal of the Acoustical Society of America 135(4). EL186–EL192. doi:10.1121/1.4865269.10.1121/1.4865269Search in Google Scholar
Karlsson, Fredrik, Katarina Olofsson, Patric Blomstedt, Jan Linder, Erik Nordh & Jan van Doorn. 2014. Articulatory closure proficiency in patients with Parkinson’s disease following deep brain stimulation of the subthalamic nucleus and caudal zona incerta. Journal of Speech, Language, and Hearing Research: JSLHR 57(4). 1178–1190. doi:10.1044/2014_JSLHR-S-13-0010.10.1044/2014_JSLHR-S-13-0010Search in Google Scholar
Klein, Olivier, Tom E. Hardwicke, Frederik Aust, Johannes Breuer, Henrik Danielsson, Alicia Hofelich Mohr, Hans Ijzerman, Gustav Nilsonne, Wolf Vanpaemel & Michael C. Frank. 2018. A practical guide for transparency in psychological science. Collabra: Psychology 4(1). 20. doi:10.1525/collabra.158.10.1525/collabra.158Search in Google Scholar
Kojima, Tsuyoshi, Shintaro Fujimura, Ryusuke Hori, Yusuke Okanoue, Kazuhiko Shoji & Masato Inoue. 2018. An innovative voice analyzer “VA” smart phone program for quantitative analysis of voice quality. Journal of Voice, 33. 642–648.10.1016/j.jvoice.2018.01.026Search in Google Scholar
Lane, Nicholas, Emiliano Miluzzo, Hong Lu, Daniel Peebles, Tanzeem Choudhury & Andrew Campbell. 2010. A survey of mobile phone sensing. IEEE Communications Magazine 48(9). 140–150. doi:10.1109/MCOM.2010.5560598.10.1109/MCOM.2010.5560598Search in Google Scholar
Leemann, Adrian, Marie-José Kolly, Ross Purves, David Britain & Elvira Glaser. 2016. Crowdsourcing language change with smartphone applications. PLoS One 11(1). e0143060. doi:10.1371/journal.pone.0143060.10.1371/journal.pone.0143060Search in Google Scholar
Lin, Emily, Jeremy Hornibrook & Tika Ormond. 2012. Evaluating iPhone recordings for acoustic voice assessment. Folia Phoniatrica et Logopaedica 64(3). 122–130. doi:10.1159/000335874.10.1159/000335874Search in Google Scholar
Manfredi, C., J. Lebacq, G. Cantarella & J. Schoentgen. 2017. Smartphones offer new opportunities in clinical voice research. Journal of Voice 31(1). 111-e1.10.1016/j.jvoice.2015.12.020Search in Google Scholar
Maryn, Youri, Femke Ysenbaert, Andrzej Zarowski & Robby Vanspauwen. 2017. Mobile communication devices, ambient noise, and acoustic voice measures. Journal of Voice 31(2). 248.e11-248.e23. doi:10.1016/j.jvoice.2016.07.023.10.1016/j.jvoice.2016.07.023Search in Google Scholar
Mazzoni, Dominic. 2018. Audacity Software. Audacity Team.Search in Google Scholar
Merwe, Anita van der. 2009. A theoretical framework for the characterization of pathological speech sensorimotor control. Clinical Management of Sensorimotor Speech Disorders. 2nd edn. New York: Thieme Medical Publishers.Search in Google Scholar
Mundt, James C., Adam P. Vogel, Douglas E. Feltner & William R. Lenderking. 2012. Vocal acoustic biomarkers of depression severity and treatment response. Biological Psychiatry 72(7). 580–587. doi:10.1016/j.biopsych.2012.03.015.10.1016/j.biopsych.2012.03.015Search in Google Scholar
Pallis, Eleftherios, Kyriaki Thermos & Christina Spyraki. 2001. Chronic desipramine treatment selectively potentiates somatostatin-induced dopamine release in the nucleus accumbens. European Journal of Neuroscience 14. 763–767.10.1046/j.0953-816x.2001.01698.xSearch in Google Scholar
Papakostas, George I. & Maurizio Fava. 2008. Predictors, moderators, and mediators (correlates) of treatment outcome in major depressive disorder. Dialogues in Clinical Neuroscience 10(4). 439–451.10.31887/DCNS.2008.10.4/gipapakostasSearch in Google Scholar
Quatieri, Thomas F & Nicolas Malyska. 2012. Vocal-source biomarkers for depression: A link to psychomotor activity. Interspeech 2012, 1059–1062, Portland, USA. Available from: https://www.isca-speech.org/archive/interspeech_2012/i12_1059.html10.21437/Interspeech.2012-311Search in Google Scholar
R Core Team. R: A language and environment for stasticial computing. Windows. Vienna, Austria: R Foundation for Statistical Computing. http://www.R-project.org/.Search in Google Scholar
Racagni, Giorgio & Maurizio Popoli. 2008. Cellular and molecular mechanisms in the long-term action of antidepressants. Dialogues in Clinical Neuroscience 10(4). 385–400.10.31887/DCNS.2008.10.4/gracagniSearch in Google Scholar
Reid, Sophie C., Sylvia D. Kauer, Stephen J.C. Hearps, Alexander H.D. Crooke, Angela S. Khor, Lena A. Sanci & George C. Patton. 2011. A mobile phone application for the assessment and management of youth mental health problems in primary care: A randomised controlled trial. BMC Family Practice 12(1). doi:10.1186/1471-2296-12-131. http://bmcfampract.biomedcentral.com/articles/10.1186/1471-2296-12-131 (29 March, 2018).10.1186/1471-2296-12-131Search in Google Scholar
Schaeffler, F. & J. Beck. 2017. Monitoring voice conditions using smartphones. Models and Analysis of Vocal Emissions for Biomedical Applications, 27–30. Firenze, Italy: Firenze University Press.Search in Google Scholar
Sheehan, D. V., Y. Lecrubier, K. H. Sheehan, P. Amorim, J. Janavs, E. Weiller, T. Hergueta, R. Baker & G. C. Dunbar. 1998. The Mini-International Neuropsychiatric Interview (M.I.N.I.): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. The Journal of Clinical Psychiatry 59(Suppl 20). 22-33;quiz 34-57.Search in Google Scholar
Stassen, H.H., S. Kuny & D. Hell. 1998. The speech analysis approach to determining onset of improvement under antidepressants. European Neuropsychopharmacology 8(4). 303–310. doi:10.1016/S0924-977X(97)00090-4.10.1016/S0924-977X(97)00090-4Search in Google Scholar
Sui, Linda. 2018. VALUE SHARE: Global Smartphone Revenue, ASP and Profit by Vendor by Price Tier: Q4 2017. Strategy Analytics. https://www.strategyanalytics.com/access-services/devices/mobile-phones/smartphone/smartphones/market-data/report-detail/value-share-global-smartphone-revenue-asp-and-profit-by-vendor-by-price-tier-q4-2017#.WpC2fahuaF60.40598907602422174 (4 February, 2019).Search in Google Scholar
Trevino, Andrea Carolina, Thomas Francis Quatieri & Nicolas Malyska. 2011. Phonologically-based biomarkers for major depressive disorder. EURASIP Journal on Advances in Signal Processing 2011(1). doi:10.1186/1687-6180-2011-42. https://asp-eurasipjournals.springeropen.com/articles/10.1186/1687-6180-2011-42 (15 February, 2019).10.1186/1687-6180-2011-42Search in Google Scholar
Uloza, Virgilijus, Evaldas Padervinskis, Aurelija Vegiene, Ruta Pribuisiene, Viktoras Saferis, Evaldas Vaiciukynas, Adas Gelzinis & Antanas Verikas. 2015. Exploring the feasibility of smart phone microphone for measurement of acoustic voice parameters and voice pathology screening. European Archives of Oto-Rhino-Laryngology 272(11). 3391–3399. doi:10.1007/s00405-015-3708-4.10.1007/s00405-015-3708-4Search in Google Scholar
Vogel, Adam P. & Paul Maruff. 2014. Monitoring change requires a rethink of assessment practices in voice and speech. Logopedics Phoniatrics Vocology 39(2). 56–61. doi:10.3109/14015439.2013.775332.10.3109/14015439.2013.775332Search in Google Scholar
Yuan, J. and M. Liberman. 2008. Speaker identification on the SCOTUS corpus. Proceedings of Acoustics ’08. Paris. Société Française d’Acoustique, 5687–690; software available at: http://www.ling.upenn.edu/phonetics/p2fa/.10.1121/1.2935783Search in Google Scholar
© 2021 Walter de Gruyter GmbH, Berlin/Boston
