Abstract
In this study, acoustic-phonetic analysis of continuous speech and statistical analyses were performed in order to find parameters in depressed speech that show significant differences compared to a healthy reference group. Read speech materials were gathered in the Hungarian and Italian languages from both healthy people and patients diagnosed with different degrees of depression. By statistical examination it was found that there are many parameters in the speech of depressed people that show significant differences compared to a healthy reference group. Moreover, most of those parameters behave similarly in other languages such as in Italian. For classification of the healthy and depressed speech, these parameters were used as an input for the classifiers. Two classification methods were compared: Support Vector Machine (SVM) and a two-layer feed-forward neural network (NN). No difference was found between the results of the two methods when trained and tested on Hungarian language (both SVM and NN classification accuracy was 75 %). In the case of training with Hungarian and testing with Italian healthy and depressed speech both classifiers reached 77 % of accuracy.
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The authors would like to thank European Space Agency COALA project: Psychological Status Monitoring by Computerised Analysis of Language phenomena (COALA) (AO-11-Concordia).
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Kiss, G., Tulics, M.G., Sztahó, D., Esposito, A., Vicsi, K. (2016). Language Independent Detection Possibilities of Depression by Speech. In: Esposito, A., et al. Recent Advances in Nonlinear Speech Processing. Smart Innovation, Systems and Technologies, vol 48. Springer, Cham. https://doi.org/10.1007/978-3-319-28109-4_11
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DOI: https://doi.org/10.1007/978-3-319-28109-4_11
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