Research Publications Authored by SLIIT Staff
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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.
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Publication Embargo Screening Tool for Autistic Children(IEEE, 2019-01-23) Tittagalla, V. Y; Wickramarachchi, R. R. P; Chandrarathne, G. W. C. N; Nanayakkara, N. M. D. M. B; Samarasinghe, P; Rathnayake, P; Pemadasa, M. G. N. MAutism is a neurological disability that has been caused due to brain abnormality in a person. A person with Autism Spectrum Disorder(ASD) usually has difficulty in social and communication skills. In the past few years there hasn't been a proper way of identifying Autistic children in Sri Lanka. In this research paper, we will discuss how to identify an autistic child by considering mobile application with the following factors. Identify the eye contact, responsiveness to stimulus, analysis of vocal behavioral patterns and questionnaire. The above four factors will be the main key areas in screening process. This tool is created especially for identifying children with autism in rural areas in Sri Lanka. The major three areas eye contact, vocal behavior and responsiveness are the screening process is developed for proof of concept in this research.Publication Embargo Machine learning based automated speech dialog analysis of autistic children(IEEE, 2019-10-24) Wijesinghe, A; Samarasinghe, P; Seneviratne, S; Yogarajah, P; Pulasinghe, KChildren with autism spectrum disorder (ASD) have altered behaviors in communication, social interaction, and activity, out of which communication has been the most prominent disorder among many. Despite the recent technological advances, limited attention has been given to screening and diagnosing ASD by identifying the speech deficiencies (SD) of autistic children at early stages. This research focuses on bridging the gap in ASD screening by developing an automated system to distinguish autistic traits through speech analysis. Data was collected from 40 participants for the initial analysis and recordings were obtained from 17 participants. We considered a three-stage processing system; first stage utilizes thresholding for silence detection and Vocal Activity Detection for vocal isolation, second stage adopts machine learning technique neural network with frequency domain representations in developing a reliant utterance classifier for the isolated vocals and stage three also adopts machine learning technique neural network in recognizing autistic traits in speech patterns of the classified utterances. The results are promising in identifying SD of autistic children with the utterance classifier having 78% accuracy and pattern recognition 72% accuracy.Publication Embargo Machine Learning Based Automated Speech Dialog Analysis Of Autistic Children(IEEE, 2019-10-24) Wijesinghe, A; Samarasinghe, P; Seneviratne, S; Yogarajah, P; Pulasinghe, KChildren with autism spectrum disorder (ASD) have altered behaviors in communication, social interaction, and activity, out of which communication has been the most prominent disorder among many. Despite the recent technological advances, limited attention has been given to screening and diagnosing ASD by identifying the speech deficiencies (SD) of autistic children at early stages. This research focuses on bridging the gap in ASD screening by developing an automated system to distinguish autistic traits through speech analysis. Data was collected from 40 participants for the initial analysis and recordings were obtained from 17 participants. We considered a three-stage processing system; first stage utilizes thresholding for silence detection and Vocal Activity Detection for vocal isolation, second stage adopts machine learning technique neural network with frequency domain representations in developing a reliant utterance classifier for the isolated vocals and stage three also adopts machine learning technique neural network in recognizing autistic traits in speech patterns of the classified utterances. The results are promising in identifying SD of autistic children with the utterance classifier having 78% accuracy and pattern recognition 72% accuracy.