A Discriminative Multi-task Learning for Autism Classification Based on Speech Signals

About 70 million people around the world are suffering from autism, which is about one in every 160 children. The causes of autism are complex, and there is no specific drug treatment. However, for an individual with autism, the earlier the age of treatment, the greater the improvement. In this paper, we collected an autism speech dataset and conducted a study on speech feature classification of autistic and normal children. We built a deep neural network, using Convolutional Recurrent Neural Network as the front-end encoder, and added Convolutional Block Attention Module to it. Integrate local features using recurrent neural networks. To prevent overfitting, we add Connectionist Temporal Classification based speech recognition auxiliary task during training. After introducing the loss function in the field of face recognition, the best classification accuracy reached 94.76%.