Autistic Language Processing by Patterns Detection

The main goal of this article is to present an approach that allows the automatic management of autistic communication patterns by processing audio and video from the therapy session of individuals suffering autistic spectrum disorders (ASD). Such patients usually have social and communication alterations that make it difficult to evaluate the meaning of those expressions. As their communicational skills may have different degrees of variation, it is very hard to understand the semantics behind the verbal behavior. The current work is based on previous work on machine learning for individual performance evaluation. Statistics show that autistic verbal behavior are physically expressed by repetitive sounds and related movements that are evident and stereotyped. The works of Leo Kanner and Ángel Riviere are also considered here. Using machine learning and neural nets with certain set of parameters, it is possible to automatically detect patterns in audio and video recording of patient's performance, which is an interesting opportunity to communicate with ASD patients.