Evaluation of Supervised Learning Algorithms Based on Speech Features as Predictors to the Diagnosis of Mild to Moderate Intellectual Disability-Reference-Cited by-同舟云学术

Evaluation of Supervised Learning Algorithms Based on Speech Features as Predictors to the Diagnosis of Mild to Moderate Intellectual Disability

Published:2018-11-08 Issue:4 Volume:9 Page:
ISSN:2092-6731
Container-title:3D Research
language:en
Short-container-title:3D Res

Author:

Aggarwal Gaurav,Singh Latika

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Software

Link

http://link.springer.com/article/10.1007/s13319-018-0207-6/fulltext.html

Reference44 articles.

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3. Aggarwal, G., & Singh, L. (2018). Classification of intellectual disability using LPC, LPCC, and WLPCC parameterization techniques. International Journal of Computers and Applications. https://doi.org/10.1080/1206212X.2018.1475330 .

4. Ai, O. C., Hariharan, M., Yaacob, S., & Chee, L. S. (2012). Classification of speech dysfluencies with MFCC and LPCC features. Expert Systems with Applications, 39(2), 2157–2165.

5. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (DSM-5 ® ). Arlington: American Psychiatric Publishing.

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