Improving ASR Systems for Children with Autism and Language Impairment Using Domain-Focused DNN Transfer Techniques

Abstract

This study explores building and improving an automatic speech recognition (ASR) system for children aged 6-9 years and diagnosed with autism spectrum disorder (ASD), language impairment (LI), or both. Working with only 1.5 hours of target data in which children perform the Clinical Evaluation of Language Fundamentals Recalling Sentences task, we apply deep neural network (DNN) weight transfer techniques to adapt a large DNN model trained on the LibriSpeech corpus of adult speech. To begin, we aim to find the best proportional training rates of the DNN layers. Our best configuration yields a 29.38% word error rate (WER). Using this configuration, we explore the effects of quantity and similarity of data augmentation in transfer learning. We augment our training with portions of the OGI Kids Corpus, adding 4.6 hours of typically developing speakers aged kindergarten through 3rd grade. We find that 2nd grade data alone - approximately the mean age of the target data - outperforms other grades and all the sets combined. Doubling the data for 1st, 2nd, and 3rd grade, we again compare each grade as well as pairs of grades. We find the combination of 1st and 2nd grade performs best at a 26.21% WER.