Genetic wrapper approach for automatic diagnosis of speech disorders related to Autism

The pervasive development disorders in autism condition lead to impairments in language and social communication. They are evidenced as atypical prosody production, emotion recognition and apraxia, among others communication deficits. This work tackle with the problem of the recognition of pathologies derived from these disorders in children, based on the acoustic analysis of speech. Specifically, the task consists of the diagnosis of normality (typically developing children) or three different pathologies. We propose an evolutionary approach to the feature selection stage. It relies on the use of genetic algorithm to find the set of features that optimally represent the speech data for this classification task. The genetic algorithm uses a support vector machine in order to evaluate the solutions (each individual) during the search. The results showed that our methodology improves the baseline provided for the task. The obtained unweighted classification accuracy was 54.80% on the development set, which represents a relative improvement of 6%, and 55.41% on test set. On the related task of binary classification between typical versus atypical developing condition, our approach achieved an unweighted classification accuracy of 92.66% on the test set.