Suprasegmental Information Modelling for Autism Disorder Spectrum and Specific Language Impairment Classification

This paper investigates the efficiency of several acoustic features in classifying pervasive developmental disorders, pervasive developmental disorders not-otherwise specified, dysphasia, and a group of control patients. One of the main characteristics of these disorders is the misuse and misrecognition of prosody in daily conversations. To capture this behaviour pitch, energy, and formants are modelled in long-term intervals, and the interval duration, shifted-delta cepstral coefficients, AM modulation index, and speaking rate complete our acoustic information. The concept of total variability space, or iVector space, is introduced as feature extractor for autism classification. This work is framed in the Interspeech 2013 Computational Paralinguistics Challenge as part of the Autism Sub challenge. Results are given on the Child Pathological Speech Database (CPSD), and an 87.6% and 45.1% unweighted average recall are obtained for the typicality (typical vs. atypical developing children) and diagnosis (classification into the 4 groups) tasks, respectively, on the development dataset. In addition, the combination of the new and the baseline features offers promising improvements.