DEEP NEURAL NETWORK APPROACHES FOR AUTISM DETECTION IN CHILDREN USING VOCAL BIOMARKERS: A SURVEY

Abstract

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by diverse social and communication challenges, with symptoms varying widely in severity. Research indicates that these challenges are often evident in the speech patterns of children with autism, making vocal biomarkers a promising avenue for early detection. Advances in machine learning, particularly deep neural networks (DNNs), offer powerful tools for analyzing these biomarkers. Despite their potential, the application of DNNs in this area remains underexplored. This survey provides a comprehensive review of the current state of DNN-based approaches, with a focus on Siamese Neural Networks, for detecting ASD through vocal biomarkers. The paper systematically examines existing speech assessment methods, evaluates the effectiveness of these neural network models, and highlights the key challenges in voice-based ASD detection. It concludes by identifying critical gaps in the research and proposing future directions to enhance the development of robust, real-world applications for early autism diagnosis.