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Prediction of Negative Symptoms of Schizophrenia from Emotion Related Low-Level Speech Signals

Citation Author(s):
Debsubhra Chakrabortyy, Zixu Yang, Yasir Tahir, Tomasz Maszczyk, Justin Dauwels, Nadia Thalmann, Jianmin Zheng, Yogeswary Maniam, Nur Amirah, Bhing Leet Tan and Jimmy Lee
Submitted by:
Debsubhra Chakr...
Last updated:
12 April 2018 - 12:16pm
Document Type:
Poster
Document Year:
2018
Event:
Presenters:
Debsubhra Chakraborty
Paper Code:
3667
 

Negative symptoms of schizophrenia are often associated with the blunting of emotional affect which creates a serious impediment in the daily functioning of the patients. Affective prosody is almost always adversely impacted in such cases, and is known to exhibit itself through the low-level acoustic signals of prosody. To automate and simplify the process of assessment of severity of emotion related symptoms of schizophrenia, we utilized these low-level acoustic signals to predict the expert subjective ratings assigned by a trained psychologist during an interview with the patient. Specifically, we extract acoustic features related to emotion using the openSMILE toolkit from the audio recordings of the interviews. We analysed the interviews of 78 paid participants (52 patients and 26 healthy controls) in this study. The subjective ratings could be accurately predicted from the objective openSMILE acoustic signals with an accuracy of 61-85% using machine-learning algorithms with leave-one-out cross-validation technique. Furthermore, these objective measures can be reliably utilized to distinguish between the patient and healthy groups, as supervised learning methods can classify the two groups with 79-86% accuracy.

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