Physiological and cognitive status monitoring on the base of acoustic-phonetic speech parameters

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8791, Issue , 2014, Pages 120-131

  Kiss, Gábor ^a   Vicsi, Klára ^a  

^a BUDAPEST UNIVERSITY OF TECHNOLOGY AND ECONOMICS   (Hungary)

Author keywords

Acoustic phonetic speech analysis; Cognitive status monitoring; Seasonal affective depression; Statistical analysis; Two sample t test

Indexed keywords

LINGUISTICS; PHYSIOLOGY; PSYCHOPHYSIOLOGY; SPEECH; STATISTICAL METHODS;

FOLLOW-UP STUDIES; LANGUAGE INDEPENDENTS; ON-LINE MONITORING SYSTEM; RESEARCH STATIONS; SEASONAL AFFECTIVE DEPRESSION; SPEECH MATERIALS; STATUS MONITORING; TWO SAMPLE T TESTS;

PHYSIOLOGICAL MODELS;

EID: 84921653015     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-11397-5_9     Document Type: Article

References (22)

1. Goberman, A.M.: Correlation between acoustic speech characteristics and non-speech motor performance in Parkinson disease. Med. Sci. Monit. 11(3), 109-116 (2005)
2. Goberman, A.M., McMillan, J.: Relative speech timing in Parkinson's disease. Commun. Sci. Disord. 32, 22-29 (2005)
3. Metter, E.J., Hanson, W.R.: Clinical and acoustical variability in hypokinetic dysarthria. J. Commun. Disord. 19(5), 347-366 (1986)
4. Doyle, P., Raade, A., Pierre, A., Desai, S.: Fundamental frequency and acoustic variability associated with production of sustained vowels by speakers with hypokinetic dysarthria. J. Med. Speech. Lang. Pathol. 3, 41-50 (1995)
5. McNeil, M.R., Rosenbeck, J.C., Aronson, A.E.: The Dysarthrias: Physiology, Acoustics, Perception, Management. College Hill Press, San Diego (1984)
6. Logemann, J.A., Fisher, H.B.: Vocal tract control in Parkinson's disease: phonetic feature analysis of misarticulations. J. Speech Hear. Disord. 46(4), 348-352 (1981)
7. Ackermann, H., Konczak, J., Hertrich, I.: The temporal control of repetitive articulatory movements in Parkinson's disease. Brain Lang. 56(2), 312-319 (1997)
8. Kent, R.D.: Acoustic Analysis of Speech, 2nd edn. Singular, San Diego (2001)
9. Lieberman, P., Morey, A., Hochstadt, J., Larson, M., Mather, S.: Mount Everest: a space analogue for speech, monitoring of cognitive deficits and stress. Aviat. Space Environ. Med. 76(6, Section II), 198-207 (2005)
10. Ivry, R.B., Justus, T.C., Middleton, C.: The cerebellum, timing, and language: implications for the study of dyslexia. In: Wolf, M. (ed.) Dyslexia Fluency and the Brain, pp. 198-211. York Press, Timonium (2001)
11. Esposito, A., Bourbakis, N.: The role of timing in speech perception and speech production processes and its effects on language impaired individuals. In: Sixth Symposium on BioInformatics and BioEngineering (BIBE'06), pp. 348-356. IEEE Computer Society (2006)
12. Vicsi, K., Sztahó, D.: Problems of the automatic emotion recognitions in spontaneous speech; an example for the recognition in a dispatcher center. In: Esposito, A., Esposito, A. M., Martone, R., Müller, V.C., Scarpetta, G. (eds.) COST 2010. LNCS, vol. 6456, pp. 331-339. Springer, Heidelberg (2011)
13. Tóth, S.L., Sztahó, D., Vicsi, K.: Speech emotion perception by human and machine. In: Esposito, A., Bourbakis, N.G., Avouris, N., Hatzilygeroudis, I. (eds.) HH and HM Interaction. LNCS (LNAI), vol. 5042, pp. 213-224. Springer, Heidelberg (2008)
14. France, D.J., Shiavi, R.G., Silverman, S., Silverman, M., Wilkes, D.M.: Acoustical properties of speech as indicators of depression and suicidal risk. IEEE Trans. Biomed. Eng. 47, 829-837 (2000)
15. Cannizzaro, M., Harel, B., Reilly, N., Chappell, P., Snyder, P.J.: Voice acoustical measurement of the severity of major depression. Brain Cogn. 56, 30-35 (2004)
16. Cannizzaro, M., Reilly, N., Mundt, J.C., Snyder, P.J.: Remote capture of human voice acoustical data by telephone: a methods study. Clin. Linguist. Phon. 19, 649-658 (2005)
17. Garcia-toro, M., Talavera, J.A., Saiz-Ruiz, J., Gonzalez, A.: Prosody impairment in depression measured through acoustic analysis. J. Nerv. Ment. Dis. 188, 824-829 (2000)
18. Abela, J.R.Z., D'Allesandro, D.U.: Beck's cognitive theory of depression: the diathesisstress and causal mediation components. Br. J. Clin. Psychol. 41, 111-128 (2002)
19. Kiss G., Sztahó D., Vicsi K.: Language independent automatic speech segmentation into phoneme-like units on the base of acoustic distinctive features. In: 4th IEEE International Conference on Cognitive Infococommunications - CogInfoCom 2013, Budapest, Hungary, 2-6 Dec 2013, pp. 579-582. IEEE Press, Piscataway (2013). ISBN: 978-1-4799-1-1543-9, IEEE Catalog Number: CFP1326R-PRT
20. Alghowinem, S., Goecke, R., Wagner, M., Epps, J., Breakspear, M., Parker, G.: Detecting depression - a comparison between spontaneous and read speech. In: 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2013)
21. Helfer, B.S., Quatieri, T.F., Williamson, J.R., Mehta, D.D., Horwitz, R., Yu, B.: Classification of depression state based on articulatory precision. In: 14th Annual Conference of the International Speech Communication Association (2013)
22. Mundt, J.C., Snyder, P.J., Cannizzaro, M.S., Chappie, K., Geralts, D.S.: Voice acoustic measures of depression severity and treatment response collected via interactive voice response (IVR) technology. J. Neurolinguist. 20, 50-64 (2007)