Detecting depression

A comparison between spontaneous and read speech

Sharifa Alghowinem, Roland Goecke, Michael Wagner, Julien Epps, Michael Breakspear, Gordon Parker

Research output: A Conference proceeding or a Chapter in BookConference contribution

42 Citations (Scopus)

Abstract

Major depressive disorders are mental disorders of high prevalence, leading to a high impact on individuals, their families, society and the economy. In order to assist clinicians to better diagnose depression, we investigate an objective diagnostic aid using affective sensing technology with a focus on acoustic features. In this paper, we hypothesise that (1) classifying the general characteristics of clinical depression using spontaneous speech will give better results than using read speech, (2) that there are some acoustic features that are robust and would give good classification results in both spontaneous and read, and (3) that a ‘thin-slicing’ approach using smaller parts of the speech data will perform similarly if not better than using the whole speech data. By examining and comparing recognition results for acoustic features on a real-world clinical dataset of 30 depressed and 30 control subjects using SVM for classification and a leave-one-out cross-validation scheme, we found that spontaneous speech has more variability, which increases the recognition rate of
depression. We also found that jitter, shimmer, energy and loudness feature groups are robust in characterising both read and spontaneous depressive speech. Remarkably, thin-slicing the read speech, using either the beginning of each sentence or the first few sentences performs better than using all reading
task data
Original languageEnglish
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
EditorsRabab Ward, Li Deng
Place of PublicationCanada
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages7547-7551
Number of pages5
ISBN (Print)9781479903566
DOIs
Publication statusPublished - 2013
Event38th International Conference on Acoustics, Speech and Signal Processing ICASSP2013 - Vancouver, Vancouver, Canada
Duration: 26 May 201331 May 2013

Conference

Conference38th International Conference on Acoustics, Speech and Signal Processing ICASSP2013
Abbreviated titleICASSP2013
CountryCanada
CityVancouver
Period26/05/1331/05/13
OtherThe ICASSP meeting is the world's largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class speakers, tutorials, exhibits, and over 50 lecture and poster sessions

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Alghowinem, S., Goecke, R., Wagner, M., Epps, J., Breakspear, M., & Parker, G. (2013). Detecting depression: A comparison between spontaneous and read speech. In R. Ward, & L. Deng (Eds.), ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (pp. 7547-7551). Canada: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICASSP.2013.6639130
Alghowinem, Sharifa ; Goecke, Roland ; Wagner, Michael ; Epps, Julien ; Breakspear, Michael ; Parker, Gordon. / Detecting depression : A comparison between spontaneous and read speech. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. editor / Rabab Ward ; Li Deng. Canada : IEEE, Institute of Electrical and Electronics Engineers, 2013. pp. 7547-7551
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abstract = "Major depressive disorders are mental disorders of high prevalence, leading to a high impact on individuals, their families, society and the economy. In order to assist clinicians to better diagnose depression, we investigate an objective diagnostic aid using affective sensing technology with a focus on acoustic features. In this paper, we hypothesise that (1) classifying the general characteristics of clinical depression using spontaneous speech will give better results than using read speech, (2) that there are some acoustic features that are robust and would give good classification results in both spontaneous and read, and (3) that a ‘thin-slicing’ approach using smaller parts of the speech data will perform similarly if not better than using the whole speech data. By examining and comparing recognition results for acoustic features on a real-world clinical dataset of 30 depressed and 30 control subjects using SVM for classification and a leave-one-out cross-validation scheme, we found that spontaneous speech has more variability, which increases the recognition rate ofdepression. We also found that jitter, shimmer, energy and loudness feature groups are robust in characterising both read and spontaneous depressive speech. Remarkably, thin-slicing the read speech, using either the beginning of each sentence or the first few sentences performs better than using all readingtask data",
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Alghowinem, S, Goecke, R, Wagner, M, Epps, J, Breakspear, M & Parker, G 2013, Detecting depression: A comparison between spontaneous and read speech. in R Ward & L Deng (eds), ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. IEEE, Institute of Electrical and Electronics Engineers, Canada, pp. 7547-7551, 38th International Conference on Acoustics, Speech and Signal Processing ICASSP2013, Vancouver, Canada, 26/05/13. https://doi.org/10.1109/ICASSP.2013.6639130

Detecting depression : A comparison between spontaneous and read speech. / Alghowinem, Sharifa; Goecke, Roland; Wagner, Michael; Epps, Julien; Breakspear, Michael; Parker, Gordon.

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. ed. / Rabab Ward; Li Deng. Canada : IEEE, Institute of Electrical and Electronics Engineers, 2013. p. 7547-7551.

Research output: A Conference proceeding or a Chapter in BookConference contribution

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AB - Major depressive disorders are mental disorders of high prevalence, leading to a high impact on individuals, their families, society and the economy. In order to assist clinicians to better diagnose depression, we investigate an objective diagnostic aid using affective sensing technology with a focus on acoustic features. In this paper, we hypothesise that (1) classifying the general characteristics of clinical depression using spontaneous speech will give better results than using read speech, (2) that there are some acoustic features that are robust and would give good classification results in both spontaneous and read, and (3) that a ‘thin-slicing’ approach using smaller parts of the speech data will perform similarly if not better than using the whole speech data. By examining and comparing recognition results for acoustic features on a real-world clinical dataset of 30 depressed and 30 control subjects using SVM for classification and a leave-one-out cross-validation scheme, we found that spontaneous speech has more variability, which increases the recognition rate ofdepression. We also found that jitter, shimmer, energy and loudness feature groups are robust in characterising both read and spontaneous depressive speech. Remarkably, thin-slicing the read speech, using either the beginning of each sentence or the first few sentences performs better than using all readingtask data

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Alghowinem S, Goecke R, Wagner M, Epps J, Breakspear M, Parker G. Detecting depression: A comparison between spontaneous and read speech. In Ward R, Deng L, editors, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Canada: IEEE, Institute of Electrical and Electronics Engineers. 2013. p. 7547-7551 https://doi.org/10.1109/ICASSP.2013.6639130