Statistical differences in speech acoustics of major depressed and non-depressed adolescents

Speech acoustic parameters have been reported to provide highly efficient diagnostic cues for depression. Current studies have been focused on characterizing the speech of adult patients but it is known that adult speech differs significantly from adolescent speech, and that the onset of depression is likely to occur during adolescence itself. This paper investigates and explains the differences in speech acoustic parameters characterizing the speech of depressed and non-depressed adolescents, namely F0 (fundamental frequency), jitter, shimmer, log energy, spectral centroid, spectral entropy and glottal pulse duration. Speech data was collected from adolescents aged 14 - 18 and included 68 (49 females and 19 males) diagnosed with major depression and 71 (44 females and 27 males) diagnosed as non-depressed. The speech recordings were made during three different types of family interactions: event-planning, problem-solving and family consensus. Statistical analysis revealed that the differences between depressed and non-depressed speech acoustics strongly depend on gender and topic of conversation. For male adolescents, the glottal pulse duration was found to be the most efficient parameter in discriminating between depressed and non-depressed speech for all three interactions. For female adolescents, the fundamental frequency F0 was the most determinant during the problem solving and family consensus interactions, whilst the glottal pulse duration was the most determinant during the event planning interaction.

[1]  P. Moses.  The Voice of Neurosis , 1954 .

[2]  P. Avignon,et al.  Causes of Death , 1884, Nature.

[3]  R. Orlikoff,et al.  The effect of the heartbeat on vocal fundamental frequency perturbation. , 1989, Journal of speech and hearing research.

[4]  J. Rabe-Jabłońska,et al.  [Affective disorders in the fourth edition of the classification of mental disorders prepared by the American Psychiatric Association -- diagnostic and statistical manual of mental disorders]. , 1993, Psychiatria polska.

[5]  Deborah A. Prentice,et al.  Rethinking Sex Differences in Aggression: Aggressive Behavior in the Absence of Social Roles , 1994 .

[6]  H. Hollien,et al.  Longitudinal research on adolescent voice change in males. , 1994, The Journal of the Acoustical Society of America.

[7]  Alan D. Lopez,et al.  The global burden of disease: a comprehensive assessment of mortality and disability from diseases injuries and risk factors in 1990 and projected to 2020. , 1996 .

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

[9]  James J. Clark,et al.  The Mental Health of Young People in Australia: Key Findings from the Child and Adolescent Component of the National Survey of Mental Health and Well-Being , 2001, The Australian and New Zealand journal of psychiatry.

[10]  A. Hanks.  Canada , 2002 .

[11]  D. Mitchell Wilkes,et al.  Investigation of vocal jitter and glottal flow spectrum as possible cues for depression and near-term suicidal risk , 2004, IEEE Transactions on Biomedical Engineering.

[12]  Elliot Moore,et al.  Algorithm for automatic glottal waveform estimation without the reliance on precise glottal closure information , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[13]  J. Peifer,et al.  Comparing objective feature statistics of speech for classifying clinical depression , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[14]  A. Paeschke.  Global Trend of Fundamental Frequency in Emotional Speech , 2004 .

[15]  Jianhua Tao,et al.  Features Importance Analysis for Emotional Speech Classification , 2005, ACII.

[16]  T. Strine,et al.  The Vital Link Between Chronic Disease and Depressive Disorders , 2004, Preventing chronic disease.

[17]  Elliot Moore,et al.  Critical Analysis of the Impact of Glottal Features in the Classification of Clinical Depression in Speech , 2008, IEEE Transactions on Biomedical Engineering.

[18]  M. Landau.  Acoustical Properties of Speech as Indicators of Depression and Suicidal Risk , 2008 .

[19]  N. Allen,et al.  Dynamics of affective experience and behavior in depressed adolescents. , 2009, Journal of child psychology and psychiatry, and allied disciplines.

[20]  Nicholas B. Allen,et al.  Mel frequency cepstral feature and Gaussian Mixtures for modeling clinical depression in adolescents , 2009, 2009 8th IEEE International Conference on Cognitive Informatics.

[21]  Nicholas B. Allen,et al.  Influence of acoustic low-level descriptors in the detection of clinical depression in adolescents , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[22]  Kuldip K. Paliwal,et al.  Preference for 20-40 ms window duration in speech analysis , 2010, 2010 4th International Conference on Signal Processing and Communication Systems.

[23]  Umaporn Chantasorn,et al.  Efficiency Comparisons of Normality Test Using Statistical Packages , 2011 .

[24]  Nicholas B. Allen,et al.  Detection of Clinical Depression in Adolescents’ Speech During Family Interactions , 2011, IEEE Transactions on Biomedical Engineering.

[25]  Nicholas B. Allen,et al.  Early prediction of major depression in adolescents using glottal wave characteristics and Teager Energy parameters , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[26]  Louis-Philippe Morency,et al.  Investigating the speech characteristics of suicidal adolescents , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[27]  Jeffrey F. Cohn,et al.  Detecting Depression Severity from Vocal Prosody , 2013, IEEE Transactions on Affective Computing.

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

[29]  Michael Wagner,et al.  Characterising depressed speech for classification , 2013, INTERSPEECH.

[30]  Nunes.  Cross-linguistic and Cultural effects on the perception of emotions , 2013 .

[31]  A. Belém.  Cross-linguistic and Cultural effects on the perception of emotions , 2013 .

[32]  Tamás D. Gedeon,et al.  A comparative study of different classifiers for detecting depression from spontaneous speech , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.