A Novel Hybrid PSO Assisted Optimization for Classification of Intellectual Disability Using Speech Signal

Speech signals convey speaker’s neurodevelopmental state along with phonological information. Recognize a speech disorder by analyzing the speech is essential for human–machine interaction. To develop a subject independent speech recognition system for neurodevelopmental disorders, by identifying voice features from MATLAB toolbox, spectral characteristics and feature selection algorithms are proposed in this paper. Feature selection is applied to overcome the challenges of dimensionality in various applications. This work presents a novel particle swarm optimization (PSO) based algorithm for feature selection. The experiments were conducted using a speech database of the children with intellectual disability with age-matched typically developed and validate the reliability using 10-fold cross-validation technique. The database consists of 141 speech features extracted from linear predictive coding (LPC) based cepstral parameters and Mel-frequency cepstral coefficients (MFCC). Three classification models were applied and obtained the recognition accuracies 90.30% with ANN, 98.00% with SVM and 91.00% with random forest with PSO feature selection algorithm. The results strongly prove the usefulness of the proposed multivariate feature selection algorithm when compared with filter approach.