alexa Detecting Depression in Speech: A Multi-classifier System with Ensemble Pruning on Kappa-Error Diagram | OMICS International| Abstract
ISSN: 2157-7420

Journal of Health & Medical Informatics
Open Access

Like us on:

OMICS International organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

Open Access Journals gaining more Readers and Citations
700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ Readers

This Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)
All submissions of the EM system will be redirected to Online Manuscript Submission System. Authors are requested to submit articles directly to Online Manuscript Submission System of respective journal.
  • Research Article   
  • J Health Med Inform 2017, Vol 8(5): 293
  • DOI: 10.4172/2157-7420.1000293

Detecting Depression in Speech: A Multi-classifier System with Ensemble Pruning on Kappa-Error Diagram

Hailiang Long#, Xia Wu#, Zhenghao Guo, Jianhong Liu and Bin Hu*
College of Information Science and Technology, Beijing Normal University, Beijing, 100875, China
#Contributed equally to this work
*Corresponding Author : Bin Hu, College of Information Science and Technology, Beijing Normal University, Beijing, 100875, China, Tel: +4155898, Email: [email protected]

Received Date: Nov 23, 2017 / Accepted Date: Nov 28, 2017 / Published Date: Nov 30, 2017

Abstract

Depression is a severe mental health disorder with high societal costs. Despite its high prevalence, its diagnostic rate is very low. To assist clinicians to better diagnose depression, researchers in recent years have been looking at the problem of automatic detection of depression from speech signals. In this study, a novel multi-classifier system for depression detection in speech was developed and tested. We collected speech data in different ways, and we examined the discriminative power of different speech types (such as reading, interview, picture description, and video description). Considering that different speech types may elicit different levels of cognitive effort and provide complementary information for the classification of depression, we can utilize various speech data sets to gain a better result for depression recognition. All individual learners formed a pool of classifiers, and some individual learners with a high diversity and accuracy in the pool were selected. In the process, the kappa-error diagram helped us make decisions. Finally, a multi-classifier system with a parallel topology was built, and each individual learner in this system used different speech data types and speech features. In our experiment, a sample of 74 subjects (37 depressed patients and 37 healthy controls) was tested and a leave-one-out cross-validation scheme was used. The experiment result showed that this new approach had a higher accuracy (89.19%) than that of single classifier methods (the best is 72.97%). In addition, we also found that the overall recognition rate using interview speech was higher than those employing picture description, video description, and reading speech. Furthermore, neutral speech showed better performance than positive and negative speech.

Keywords: Depression detection; Speech signal; Multi-classifier system; Kappa-Error Diagram

Citation: Long H, Wu X, Guo Z, Liu J, Hu B (2017) Detecting Depression in Speech: A Multi-classifier System with Ensemble Pruning on Kappa-Error Diagram. J Health Med Informat 8: 293. Doi: 10.4172/2157-7420.1000293

Copyright: © 2017 Long H, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Select your language of interest to view the total content in your interested language

Post Your Comment Citation
Share This Article
Article Usage
  • Total views: 2927
  • [From(publication date): 0-2017 - May 26, 2019]
  • Breakdown by view type
  • HTML page views: 2793
  • PDF downloads: 134
Leave Your Message 24x7
Top