Non-verbal speech cues as objective measures for negative symptoms in patients with schizophrenia

被引:37
|
作者
Tahir, Yasir [1 ]
Yang, Zixu [2 ]
Chakraborty, Debsubhra [1 ]
Thalmann, Nadia [1 ]
Thalmann, Daniel [1 ]
Maniam, Yogeswary [2 ]
Rashid, Nur Amirah Binte Abdul [2 ]
Tan, Bhing-Leet [2 ,4 ]
Keong, Jimmy Lee Chee [2 ,3 ]
Dauwels, Justin [5 ]
机构
[1] Nanyang Technol Univ, Inst Media Innovat, Singapore, Singapore
[2] Inst Mental Hlth, Singapore, Singapore
[3] Nanyang Technol Univ, Lee Kong Chian Sch Med, Singapore, Singapore
[4] Singapore Inst Technol, Singapore, Singapore
[5] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore, Singapore
来源
PLOS ONE | 2019年 / 14卷 / 04期
基金
新加坡国家研究基金会;
关键词
FLAT AFFECT; 1ST-EPISODE; DEFINITION; BOUNDARIES; BEHAVIOR; DEFICITS;
D O I
10.1371/journal.pone.0214314
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Negative symptoms in schizophrenia are associated with significant burden and possess little to no robust treatments in clinical practice today. One key obstacle impeding the development of better treatment methods is the lack of an objective measure. Since negative symptoms almost always adversely affect speech production in patients, speech dysfunction have been considered as a viable objective measure. However, researchers have mostly focused on the verbal aspects of speech, with scant attention to the non-verbal cues in speech. In this paper, we have explored non-verbal speech cues as objective measures of negative symptoms of schizophrenia. We collected an interview corpus of 54 subjects with schizophrenia and 26 healthy controls. In order to validate the non-verbal speech cues, we computed the correlation between these cues and the NSA-16 ratings assigned by expert clinicians. Significant correlations were obtained between these non-verbal speech cues and certain NSA indicators. For instance, the correlation between Turn Duration and Restricted Speech is -0.5, Response time and NSA Communication is 0.4, therefore indicating that poor communication is reflected in the objective measures, thus validating our claims. Moreover, certain NSA indices can be classified into observable and non-observable classes from the non-verbal speech cues by means of supervised classification methods. In particular the accuracy for Restricted speech quantity and Prolonged response time are 80% and 70% respectively. We were also able to classify healthy and patients using nonverbal speech features with 81.3% accuracy.
引用
收藏
页数:17
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