Abstract
Major Depressive Disorder (MDD) is a largely extended mental health disorder commonly associated with a hesitant and monotonous speech. This study analyses a speech corpus from a database acquired on 40 MDD patients and 40 matched controls (CT). During the recordings, individuals experienced different levels of cognitive stress when performing Stroop color test that includes three tasks with increasingly level of difficulty. Speech features based on the fundamental frequency (F0), and the speech ratio (SR), which measures the speech to silence ratio, are used for characterising depressive mood and stress responsiveness. Results show that SR is significantly lower in MDD subjects compared to healthy controls for all the tasks, decreasing as the difficulty of the cognitive tasks, and thus the stress level, increases. Moreover F0 related parameters (median and interquartile range) show higher values within the same subject in tasks with increased difficulty level for both groups. It can be concluded that speech features could be used for characterising depressive mood and assessing different levels of stress.
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Acknowledgements
This work has been supported by AEI and FEDER under the projects RTI2018-097723-B-I00 and 2014–2020 “Building Europe from Aragón”, by CIBER de Bioingeniería, Biomateriales y Nanomedicina, and CIBERSAM, through Instituto de Salud Carlos III, by LMP44-18, BSICoS group (T39-20R), ViVoLab group (T36-20R) and a personal grant to S. Kontaxis funded by Gobierno de Aragón; and by Spanish Ministry of Economy and Competitiveness and the European Social Fund (TIN2017-85854-C4-1-R). The computation was performed by the ICTS ‘NANBIOSIS’, more specifically by the High Performance Computing Unit of the CIBER in Bioengineering, Biomaterials & Nanomedicne (CIBERBBN).
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Martínez, C. et al. (2021). Analysis of Prosodic Features During Cognitive Load in Patients with Depression. In: D'Haro, L.F., Callejas, Z., Nakamura, S. (eds) Conversational Dialogue Systems for the Next Decade. Lecture Notes in Electrical Engineering, vol 704. Springer, Singapore. https://doi.org/10.1007/978-981-15-8395-7_14
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