A Persian speaker-independent dataset to diagnose autism infected children based on speech processing techniques

• Published in: 2021 7th International Conference on Signal Processing and Intelligent Systems (ICSPIS) pp. 01 - 05
• Main Authors: Alizadeh, Maryam, Tabibian, Shima
• Format: Conference Proceeding
• Language: English
• Published: IEEE 29.12.2021
• Subjects: Autism; Data collection; Diagnose speech abnormalities; Hidden Markov Model; Markov processes; Pediatrics; Phonetics; Signal processing; Speech Database; speech processing; Speech recognition
• DOI: 10.1109/ICSPIS54653.2021.9729345

Autism spectrum disorder is one kind of brain developmental disorders. The easiest way to diagnose persons with autism is done through speech processing techniques. However, limited researches have been done in this field. The reason may be due to the lack of valid and suitable datasets in this field. Therefore, in this paper, while analyzing the existing datasets in this field, the process of designing, collecting and evaluating a Persian speaker-independent dataset to diagnose children with autism (PersionSIChASD dataset) using speech processing methods has been discussed. Data collection has been done under the supervision of an autism specialist. The dataset includes those phonetic units that children with autism have difficulty in saying them, correctly. The results of evaluating the proposed dataset have shown speech recognition accuracies equal to 76% and 12% for phonetic units articulated by typical and autism infected children, respectively. The significant difference between the mentioned recognition rates (about 64%) could be exploited to diagnose autism infected children.