IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Comparisons of Speech Parameterisation Techniques for Classification of Intellectual Disability Using Machine Learning

Comparisons of Speech Parameterisation Techniques for Classification of Intellectual Disability Using Machine Learning
View Sample PDF
Author(s): Gaurav Aggarwal (Manipal University Jaipur, Jaipur, India)and Latika Singh (Ansal University, Gurgaon, India)
Copyright: 2020
Volume: 14
Issue: 2
Pages: 19
Source title: International Journal of Cognitive Informatics and Natural Intelligence (IJCINI)
Editor(s)-in-Chief: Kangshun Li (South China Agricultural University, China)
DOI: 10.4018/IJCINI.2020040102

Purchase


Abstract

Classification of intellectually disabled children through manual assessment of speech at an early age is inconsistent, subjective, time-consuming and prone to error. This study attempts to classify the children with intellectual disabilities using two speech feature extraction techniques: Linear Predictive Coding (LPC) based cepstral parameters, and Mel-frequency cepstral coefficients (MFCC). Four different classification models: k-nearest neighbour (k-NN), support vector machine (SVM), linear discriminant analysis (LDA) and radial basis function neural network (RBFNN) are employed for classification purposes. 48 speech samples of each group are taken for analysis, from subjects with a similar age and socio-economic background. The effect of the different frame length with the number of filterbanks in the MFCC and different frame length with the order in the LPC is also examined for better accuracy. The experimental outcomes show that the projected technique can be used to help speech pathologists in estimating intellectual disability at early ages.

Related Content

Fahong Yu, Meijia Chen, Bolin Yu. © 2023. 16 pages.
Yi Wang, Kangshun Li. © 2023. 18 pages.
Kangshun Li, Leqing Lin, Jiaming Li, Siwei Chen, Hassan Jalil. © 2023. 11 pages.
Hong-Bo Wang, Wei Huang. © 2023. 17 pages.
Manik Hendre, Prasenjit Mukherjee, Raman Preet, Manish Godse. © 2023. 14 pages.
Sanfeng Chen, Guangming Lin, Tao Hu, Hui Wang, Zhouyi Lai. © 2023. 13 pages.
Jiang Chong. © 2023. 18 pages.
Body Bottom