A Speech Emotion Recognition Solution-based on Support Vector Machine for Children with Autism Spectrum Disorder to Help Identify Human Emotions

Abstract

Children who fall into the autism spectrum have difficulty communicating with others. In this work, a speech emotion recognition model has been developed to help children with Autism Spectrum Disorder (ASD) identify emotions in social interactions. The model is created using the Python programming language to develop a machine learning model based on the Support Vector Machine (SVM). SVM has proven to yield high accuracies when classifying inputs in speech processing. Individual audio databases are specifically designed to train models for the emotion recognition task. One such speech corpus is the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), which is used to train the model in this work. Acoustic feature extraction will be part of the pre-processing step utilizing Python libraries. The libROSA library is used in this work. The first 26 Mel-frequency Cepstral Coefficients (MFCCs) and the zero-crossing rate (ZCR) are extracted and used as the acoustic features to train the machine learning model. The final SVM model provided a test accuracy of 77%. This model also performed well when significant background noise was introduced to the RAVDESS audio recordings, for which it yielded a test accuracy of 64%.