Automatic Classification of Autistic Child Vocalisations: A Novel Database and Results

Humanoid robots have in recent years shown great promise for supporting the educational needs of children on the autism spectrum. To further improve the efficacy of such interactions, user-adaptation strategies based on the individual needs of a child are required. In this regard, the proposed study assesses the suitability of a range of speech-based classification approaches for automatic detection of autism severity according to the commonly used Social Responsiveness ScaleTM second edition (SRS2). Autism is characterised by socialisation limitations including child language and communication ability. When compared to neurotypical children of the same age these can be a strong indication of severity. This study introduces a novel dataset of 803 utterances recorded from 14 autistic children aged between 4 – 10 years, during Wizard-of-Oz interactions with a humanoid robot. Our results demonstrate the suitability of support vector machines (SVMs) which use acoustic feature sets from multiple Interspeech COMPARE challenges. We also evaluate deep spectrum features, extracted via an image classification convolutional neural network (CNN) from the spectrogram of autistic speech instances. At best, by using SVMs on the acoustic feature sets, we achieved a UAR of 73.7 % for the proposed 3-class task.