Comparative Study of Filter Characteristics as Statistical Vocal Correlates of Clinical Psychiatric State in Human
Acoustical properties of speech have been shown to
be related to mental states of speaker with symptoms: depression
and remission. This paper describes way to address the issue of
distinguishing depressed patients from remitted subjects based on
measureable acoustics change of their spoken sound. The vocal-tract
related frequency characteristics of speech samples from female
remitted and depressed patients were analyzed via speech
processing techniques and consequently, evaluated statistically by
cross-validation with Support Vector Machine. Our results
comparatively show the classifier's performance with effectively
correct separation of 93% determined from testing with the subjectbased
feature model and 88% from the frame-based model based on
the same speech samples collected from hospital visiting interview
sessions between patients and psychiatrists.
[1] M. Hamilton, "A rating scale for depression", Journal of Neurology,
Neurosurgery and Psychiatry, Vol. 23, pp. 56-62, 1960
[2] France, D.J., et al., "Acoustical properties of speech as indicators of
depression and suicide", IEEE transactions on BME, 2000. 47:p 829-837.
[3] Ozdas, A., et al., "Analysis of Vocal Tract Characteristics for Near-term
Suicidal Risk Assessment", Meth.Info.in Medicine, 2004. 43: p. 36-38.
[4] Ozdas, A., et al., "Investigation of Vocal Jitter and Glottal Flow Spectrum
as Possible Cues for Depression and Near-Term Suicidal Risk", IEEE
Transactions on BME, 2004. 51: p. 1530-1540.
[5] T. Yingthawornsuk, H. Kaymaz Keskinpala, D. France, D. M. Wilkes, R.
G. Shiavi, R.M. Salomon, "Objective Estimation of Suicidal Risk using
Vocal Output Characteristics", International Conference on Spoken
Language Processing (ICSLP-Interspeech 2006), 2006, pp. 649-652.
[6] T. Yingthawornsuk, et al., "Direct Acuostic Feature using Iterative EM
Algorithm and Spectral Energy for Classifying Suicidal Risk",
Interspeech 2007, Antwerp, Belguim.
[7] F. Tolkmitt, H. Helfrich, R. Standke, K.R. Scherer, "Vocal Indicators of
Psychiatric Treatment Effects in Depressives and Schizophrenics", J.
Communication Disorders, Vol.15, pp.209-222, 1982.
[8] G. Fairbanks, Voice and Articulation Drillbook. Harper &Row, New
York, 1960.
[9] A.T. Beck, st al., "An inventory for measuring depression", Arch Gen
Psychiatry, 1961. 4:p. 561-571
[10] Dempster, A.P., et al., "Maximum likelihood from incomplete data via the
EM algorithm", J. Royal Stat. Soc. Series B, 39:1-38, 1977.
[11] V.N. Vapnik, The Natural of Statistical Learning Theory. 2nd ed.,
Springer Verlag (New York), Dec 1999
[12] C. Cortes and V.N. Vapnik , "Support vector networks", Machine
Learning, vol.20, pp. 1-25, 1995.
[13] A.J. Richard, Applied Multivariate Statistical Analysis. 3th ed., Prentice
hall, New Jersey, 1992
[1] M. Hamilton, "A rating scale for depression", Journal of Neurology,
Neurosurgery and Psychiatry, Vol. 23, pp. 56-62, 1960
[2] France, D.J., et al., "Acoustical properties of speech as indicators of
depression and suicide", IEEE transactions on BME, 2000. 47:p 829-837.
[3] Ozdas, A., et al., "Analysis of Vocal Tract Characteristics for Near-term
Suicidal Risk Assessment", Meth.Info.in Medicine, 2004. 43: p. 36-38.
[4] Ozdas, A., et al., "Investigation of Vocal Jitter and Glottal Flow Spectrum
as Possible Cues for Depression and Near-Term Suicidal Risk", IEEE
Transactions on BME, 2004. 51: p. 1530-1540.
[5] T. Yingthawornsuk, H. Kaymaz Keskinpala, D. France, D. M. Wilkes, R.
G. Shiavi, R.M. Salomon, "Objective Estimation of Suicidal Risk using
Vocal Output Characteristics", International Conference on Spoken
Language Processing (ICSLP-Interspeech 2006), 2006, pp. 649-652.
[6] T. Yingthawornsuk, et al., "Direct Acuostic Feature using Iterative EM
Algorithm and Spectral Energy for Classifying Suicidal Risk",
Interspeech 2007, Antwerp, Belguim.
[7] F. Tolkmitt, H. Helfrich, R. Standke, K.R. Scherer, "Vocal Indicators of
Psychiatric Treatment Effects in Depressives and Schizophrenics", J.
Communication Disorders, Vol.15, pp.209-222, 1982.
[8] G. Fairbanks, Voice and Articulation Drillbook. Harper &Row, New
York, 1960.
[9] A.T. Beck, st al., "An inventory for measuring depression", Arch Gen
Psychiatry, 1961. 4:p. 561-571
[10] Dempster, A.P., et al., "Maximum likelihood from incomplete data via the
EM algorithm", J. Royal Stat. Soc. Series B, 39:1-38, 1977.
[11] V.N. Vapnik, The Natural of Statistical Learning Theory. 2nd ed.,
Springer Verlag (New York), Dec 1999
[12] C. Cortes and V.N. Vapnik , "Support vector networks", Machine
Learning, vol.20, pp. 1-25, 1995.
[13] A.J. Richard, Applied Multivariate Statistical Analysis. 3th ed., Prentice
hall, New Jersey, 1992
@article{"International Journal of Medical, Medicine and Health Sciences:63337", author = "Thaweesak Yingthawornsuk and Chusak Thanawattano", title = "Comparative Study of Filter Characteristics as Statistical Vocal Correlates of Clinical Psychiatric State in Human", abstract = "Acoustical properties of speech have been shown to
be related to mental states of speaker with symptoms: depression
and remission. This paper describes way to address the issue of
distinguishing depressed patients from remitted subjects based on
measureable acoustics change of their spoken sound. The vocal-tract
related frequency characteristics of speech samples from female
remitted and depressed patients were analyzed via speech
processing techniques and consequently, evaluated statistically by
cross-validation with Support Vector Machine. Our results
comparatively show the classifier's performance with effectively
correct separation of 93% determined from testing with the subjectbased
feature model and 88% from the frame-based model based on
the same speech samples collected from hospital visiting interview
sessions between patients and psychiatrists.", keywords = "Depression, SVM, Vocal Extract, Vocal Tract", volume = "4", number = "12", pages = "566-6", }