Support-Vector Machine-Based Classifier of Cross-Correlated Phoneme Segments for Speech Sound Disorder Screening

Abstract

This paper presents a Support-Vector Machine (SVM) based method of classification of cross-correlated phoneme segments as part of the development of an automated Speech Sound Disorder (SSD) Screening tool. The pre-processing stage of the algorithm uses cross-correlation to segment the target phoneme and extracts data from the new homogeneously trimmed audio samples. Such data is then fed into the SVM-based classification script which currently achieves an accuracy of 97.5% on a dataset of 132 rows. Given the global context of an increasing trend in the incidence of Speech Sound Disorders (SSDs) amongst early-school aged children (5-6 years old), the constraints imposed by the new Corona virus pandemic, and the (consequent) shortage of professionally trained specialists, an automated screening tool would be of much assistance to Speech-Language Pathologists (SLPs).