Acoustic feature selection utilizing multiple kernel learning for classification of children with autism spectrum and typically developing children

This paper reports the result of a classification experiment carried out using acoustic features for children with autism spectrum, where a new feature-weighting method using a multiple kernel learning (MKL) algorithm is proposed for classification between children with autism spectrum and typically developing children. Our MKL-SVM simultaneously estimates both the classification boundary and weight of each acoustic feature, where 484 acoustic features are used in our experiments. The estimated weight indicates how acoustic features are useful for classification. Our results show the large weight acoustic features mainly for line spectral frequencies in the classification experiment using acoustic features for children with autism spectrum.