Physiological and Cognitive Status Monitoring on the Base of Acoustic-Phonetic Speech Parameters

In this paper the development of an online monitoring system is shown in order to track physiological and cognitive condition of crew members of the Concordia Research Station in Antarctica, with specific regard to depression. Follow-up studies were carried out on recorded speech material in such a way that segmental and supra-segmental speech parameters were measured for individual researchers weakly, and the changes of these parameters were detected over time. Two kind of speech were recorded weekly by crew members in their mother tongue: a diary and a tale (“North Wind and The Sun”). An automatic language independent program was used to segment the records in phoneme level for the measurements. Such a way Concordia Speech Databases were constructed. Those acoustic-phonetic parameters were selected for the follow up study at Concordia, which parameters were statistically selected during a research on the base of the analysis of Seasonal Affective Disorder Databases gathered separately in Europe.

[1]  Michael Wagner,et al.  Detecting depression: A comparison between spontaneous and read speech , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[2]  Klára Vicsi,et al.  Problems of the Automatic Emotion Recognitions in Spontaneous Speech; An Example for the Recognition in a Dispatcher Center , 2010, COST 2102 Training School.

[3]  Michael Cannizzaro,et al.  Voice acoustical measurement of the severity of major depression , 2004, Brain and Cognition.

[4]  J. A. Talavera,et al.  Prosody impairment in depression measured through acoustic analysis. , 2000, The Journal of nervous and mental disease.

[5]  A. Goberman,et al.  Correlation between acoustic speech characteristics and non-speech motor performance in Parkinson Disease. , 2005, Medical science monitor : international medical journal of experimental and clinical research.

[6]  Hermann Ackermann,et al.  The Temporal Control of Repetitive Articulatory Movements in Parkinson's Disease , 1997, Brain and Language.

[7]  Philip Lieberman,et al.  Mount Everest: a space analogue for speech monitoring of cognitive deficits and stress. , 2005, Aviation, space, and environmental medicine.

[8]  Klára Vicsi,et al.  Speech Emotion Perception by Human and Machine , 2008, COST 2102 Workshop.

[9]  James C Mundt,et al.  Remote capture of human voice acoustical data by telephone: A methods study , 2005, Clinical linguistics & phonetics.

[10]  Raymond D. Kent,et al.  Acoustic Analysis of Speech , 2009 .

[11]  Thomas F. Quatieri,et al.  Classification of depression state based on articulatory precision , 2013, INTERSPEECH.

[12]  D. Mitchell Wilkes,et al.  Acoustical properties of speech as indicators of depression and suicidal risk , 2000, IEEE Transactions on Biomedical Engineering.

[13]  Nikolaos G. Bourbakis,et al.  Verbal and Nonverbal Features of Human-Human and Human-Machine Interaction, COST Action 2102 International Conference, Patras, Greece, October 29-31, 2007. Revised Papers , 2008, COST 2102 Workshop.

[14]  Anna Esposito,et al.  Towards Autonomous, Adaptive, and Context-Aware Multimodal Interfaces: Theoretical and Practical Issues , 2011 .

[15]  Nikolaos G. Bourbakis,et al.  The Role of Timing in Speech Perception and Speech Production Processes and its Effects on Language Impaired Individuals , 2006, Sixth IEEE Symposium on BioInformatics and BioEngineering (BIBE'06).

[16]  E. Metter,et al.  Clinical and acoustical variability in hypokinetic dysarthria. , 1986, Journal of communication disorders.

[17]  J. Mundt,et al.  Voice acoustic measures of depression severity and treatment response collected via interactive voice response (IVR) technology , 2007, Journal of Neurolinguistics.

[18]  Susan E. Gathercole,et al.  Theoretical and Practical Issues , 2014 .

[19]  David U. D'Alessandro,et al.  Beck's cognitive theory of depression: a test of the diathesis-stress and causal mediation components. , 2002, The British journal of clinical psychology.

[20]  J. Logemann,et al.  Vocal Tract Control in Parkinson's Disease , 1981 .

[21]  A. Aronson,et al.  The Dysarthrias: Physiology, Acoustics, Perception, Management , 1985 .

[22]  Klara Vicsi,et al.  Language independent automatic speech segmentation into phoneme-like units on the base of acoustic distinctive features , 2013, 2013 IEEE 4th International Conference on Cognitive Infocommunications (CogInfoCom).