Analysis and classification of speech sounds of children with autism spectrum disorder using acoustic features. (March 2022)

• Record Type: Journal Article
• Authors: Mohanta, Abhijit; Mittal, Vinay Kumar
• Is Part Of: Computer speech & language. Volume 72(2022)
• Publication Date: 2022-03
• Subjects: Autism spectrum disorder -- English vowels -- Formant frequencies -- Dominant frequencies -- Strength of excitation -- Probabilistic neural network -- p-value; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2021.101287
• Languages: English
• ISSNs: 0885-2308

Abstract

Abstract: Children with autism spectrum disorder (ASD) produce speech sounds different from that of Normal or non-ASD children. Hence, analyzing acoustic features can help characterizing the ASD speech signals. In this study, the distinguishing characteristics of speech production are examined for ASD affected children, with comparison to Normal children's speech. Acoustic features are analyzed first and then classification of ASD vs Normal speech is attempted using different machine learning techniques. Two speech sound databases are recorded for this study: the speech database of children with ASD and the speech database of Normal children. English speech utterances are recorded for children of Indian regional (Tamil and Telugu) nativity. The changes due to autism effect are examined in context of 5 English vowel sounds (/a/, /e/, /i/, /o/, and /u/). Changes in the speech production characteristics of children are explored using three sets of features. Firstly, changes in the excitation source features are examined using strength of excitation (SoE) and instantaneous fundamental frequency (F0). Secondly, changes in the vocal tract (VT) filter features are examined using dominant frequencies (FD1, FD2) and formant frequencies (F1 to F5). Thirdly, changes in the source-filter combined features are examined using signal energy (E), zero-crossing rate (ZCR), linear prediction cepstrum coefficients (LPCC), and Mel-frequency cepstral coefficients (MFCC). Then, various combinations of the acoustic features are classified utilizing machine learning methods such as probabilistic neural network (PNN), multilayer perceptron (MLP), support vector machine (SVM), and K-nearest neighbors (KNN). Analyses of acoustic features shows significant differences between the speech of children with ASD and the Normal children. Results up to 98.17% accuracy are obtained for classification between acoustic features of the speech sounds of children with ASD and the Normal children. The observations and this study results may be useful as acoustic biomarkers to identify autism and its progression/cure among children. This study may also be valuable towards developing a system for ASD diagnosis from children's speech sounds, in the future. Highlights: This study considered the native Indian speakers (children) with ASD. Two speech sound databases are recorded for this study purpose. Unlike previous studies, here a new approach is followed to record the databases. English vowel sounds (/a/, /e/, /i/, /o/, and /u/) of ASD affected children are explored here. A few results of acoustic features are explained in the context of speech production mechanism.