An Audio Processing Approach using Ensemble Learning for Speech-Emotion Recognition for Children with ASD

Abstract

Children with Autism Spectrum Disorder (ASD) find it difficult to detect human emotions in social interactions. A speech emotion recognition system was developed in this work, which aims to help these children to better identify the emotions of their communication partner. The system was developed using machine learning and deep learning techniques. Through the use of ensemble learning, multiple machine learning algorithms were joined to provide a final prediction on the recorded input utterances. The ensemble of models includes a Support Vector Machine (SVM), a Multi-Layer Perceptron (MLP), and a Recurrent Neural Network (RNN). All three models were trained on the Ryerson Audio-Visual Database of Emotional Speech and Songs (RAVDESS), the Toronto Emotional Speech Set (TESS), and the Crowd-sourced Emotional Multimodal Actors Dataset (CREMA-D). A fourth dataset was used, which was created by adding background noise to the clean speech files from the datasets previously mentioned. The paper describes the audio processing of the samples, the techniques used to include the background noise, and the feature extraction coefficients considered for the development and training of the models. This study presents the performance evaluation of the individual models to each of the datasets, inclusion of the background noises, and the combination of using all of the samples in all three datasets. The evaluation was made to select optimal hyperparameters configuration of the models to evaluate the performance of the ensemble learning approach through majority voting. The overall performance of the ensemble learning reached a peak accuracy of 66.5%, reaching a higher performance emotion classification accuracy than the MLP model which reached 65.7%.