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Automated ASD detection in children from raw speech using customized STFT-CNN model

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Abstract

Autism spectrum disorder (ASD), a prevalent neurodevelopmental condition impacting cognitive, communicative, and behavioral aspects, typically manifests in early childhood due to genetic, environmental, and immunological factors. Employing a novel dataset termed children’s ASD speech corpus (CASD-SC), the research makes use of short-time Fourier transform (STFT) layered convolutional neural networks (CNN), incorporating an image input layer and a sequence input layer. The analysis encompasses data both with and without augmentation, exploring various CNN configurations. Results showcase that the log spectrogram-based STFT layered CNN model achieves 86.6% accuracy for the raw data, while the pre-emphasis filter (PEF) with learnables-based STFT layered CNN model attains 99.1% accuracy for the data with augmentation for detecting ASD in children. This investigation bridges the literature gap by evaluating child-specific raw speech data. The study underscores the significance of processing and training efficiency in ASD diagnosis and promotes early intervention techniques by improving ASD detection in children.

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Acknowledgements

The authors would like to acknowledge the collection of healthy children datasets from Maddi Subba Rao English Medium High School, Vijayawada, under the supervision of the Principal Mrs. Potnuri Syamala. Additionally, the collection of ASD children datasets was facilitated by Home Occupational Therapy Services, Bharathi Nagar, Vijayawada, under the direction of Dr. Sushil Kumar, and Autism Child Guidance Center, Vijayawada, under the guidance of Director Mr. Praveen Kumar. Their cooperation and support were invaluable to the completion of this research project.

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Authors and Affiliations

  1. Department of Electronics & Communication Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Kanuru, Vijayawada, Andhra Pradesh, 520007, India

    Kurma Venkata Keerthana Sai, Rompicharla Thanmayee Krishna, Kodali Radha, Dhulipalla Venkata Rao & Abdul Muneera

  2. Division of Pediatric Neurology, Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA

    Kodali Radha

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  1. Kurma Venkata Keerthana Sai
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Correspondence to Kodali Radha.

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Sai, K.V.K., Krishna, R.T., Radha, K. et al. Automated ASD detection in children from raw speech using customized STFT-CNN model. Int J Speech Technol 27, 701–716 (2024). https://doi.org/10.1007/s10772-024-10131-7

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