Classification of intellectual disability using LPC, LPCC, and WLPCC parameterization techniques

Abstract

ABSTRACTThe goal of this study is to present a comparative analysis between the three feature extraction techniques: Linear Predictive Coefficients (LPC), Linear Prediction Cepstral Coefficients (LPCC), and Weighted Linear Prediction Cepstral Coefficients (WLPCC) for distinguishing speech of Intellectually Disabled (ID) children from Typically Developed (TD). Speech samples of ID children were recorded from a government-owned special school in India and used for the analysis. Control samples were taken from the author’s institute. Pre-processing techniques were used for better parameterization. Two classifiers, Artificial Neural Network (ANN) and Linear Discriminant Analysis , were applied to classify between disordered and the normal speech. Cross-validation technique was used to evaluate the reliability of the classifier. This study has discussed the effect on the classification accuracy using different values of frame size, percentage of frame overlapping, α value of pre-emphasis first-order filter and the order p. The experimental calculation interpreted that all the three parameterization techniques can be used for the correct classification of the ID speech from controls and WLPCC features slightly outperform than LPCC and considerably outperforms than LPC features.