Articulation Battery Test For Tamil Speech Disorder Signals

Abstract

Articulation disorder is referred as difficulty occurs in the pronunciation of specific speech sounds. An irregular coordination of the movement of tongue, lips, palate, jaw, respiratory system, vocal tract, height of the larynx, air flow through nasal leads to the incorrect production of speech sounds. The objective of this paper is to propose a computational model based on Recurrent Neural Network (RNN) algorithm to categorize the phonological patterns of Tamil speech articulation disorder signals into four predefined groups, namely, substitution, omission, distortion and addition. The methodology of the proposed work is described as follows. (1) List of articulation disorder test words suggested by Speech Language Pathologists (SLPs) is selected for this experimental study. (2) Real time speech signals that comprise of Tamil vowels (Uyir eluthukkal) and consonants (Meiyeluthukkal) are collected from people with articulation disorder. (3) Acoustic noise and weak signals are eliminated by applying Low pass filter to acquire the filtered speech signal. (4) Mel-Frequency Cepstral Coefficients (MFCCs) technique is implemented to extract the prominent features from denoised signals. (5) Principal Component Analysis (PCA) method is employed to choose fine-tune feature subset. (6) The refined features are employed to calibrate RNN model for classification. Results show that RNN model achieves 90.25% classification accuracy when compared to other artificial neural network algorithms.