Robust and Accurate Features for Detecting and Diagnosing Autism Spectrum Disorders.

In this paper, we report experiments on the Interspeech 2013 Autism Challenge, which comprises of two subtasks - detecting children with ASD and classifying them into four subtypes. We apply our recently developed algorithm to extract speech features that overcomes certain weaknesses of other currently available algorithms [1, 2]. From the input speech signal, we estimate the parameters of a harmonic model of the voiced speech for each frame including the fundamental frequency (f 0). From the fundamental frequencies and the reconstructed noise-free signal, we compute other derived features such as Harmonic-to-Noise Ratio (HNR), shimmer, and jitter. In previous work, we found that these features detect voiced segments and speech more accurately than other algorithms and that they are useful in rating the severity of a subject's Parkinson's disease [3]. Here, we employ these features, along with standard features such as energy, cepstral, and spectral features. With these features, we detect ASD using a regression and identify the sub-type using a classifier. We find that our features improve the performance, measured in terms of unweighted average recall (UAR), of detecting autism spectrum disorder by 2.3% and classifying the disorder into four categories by 2.8% over the baseline results.