Abhijit Mohanta


2019

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Autism Speech Analysis using Acoustic Features
Abhijit Mohanta | Vinay Kumar Mittal
Proceedings of the 16th International Conference on Natural Language Processing

Autism speech has distinct acoustic patterns, different from normal speech. Analyzing acoustic features derived from the speech of children affected with autism spectrum disorder (ASD) can help its early detection. In this study, a comparative analysis of the discriminating acoustic characteristics is carried out between ASD affected and normal children speech, from speech production point of view. Datasets of English speech of children affected with ASD and normal children were recorded. Changes in the speech production characteristics are examined using the excitation source features F0 and strength of excitation (SoE), the vocal tract filter features formants (F1 to F5) and dominant frequencies (FD1, FD2), and the combined source-filter features signal energy and zero-crossing rate. Changes in the acoustic features are compared in the five vowels’ regions of the English language. Significant changes in few acoustic features are observed for ASD affected speech as compared to normal speech. The differences between the mean values of the formants and dominant frequencies, for ASD affected and normal children, are highest for vowel /i/. It indicates that ASD affected children have possibly more difficulty in speaking the words with vowel /i/. This study can be helpful towards developing systems for automatic detection of ASD.

2018

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Analyzing Autism Speech of Children in English Vowels Regions by Analysis of Changes in Production Features
Abhijit Mohanta | Vinay Kumar Mittal
Proceedings of the 15th International Conference on Natural Language Processing

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