Estimating Autism Severity in Young Children from Speech Signals Using a Deep Neural Network

Marina Eni, Ilan Dinstein, Michal Ilan, Idan Menashe, Gal Meiri, Yaniv Zigel

Research output: Contribution to journalArticlepeer-review

39 Scopus citations


Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that involves difficulties in social communication. Previous research has demonstrated that these difficulties are apparent in the way ASD children speak, indicating that it may be possible to estimate ASD severity using quantitative features of speech. Here, we extracted a variety of prosodic, acoustic, and conversational features from speech recordings of Hebrew speaking children who completed an Autism Diagnostic Observation Schedule (ADOS) assessment. Sixty features were extracted from the recordings of 72 children and 21 of the features were significantly correlated with the children's ADOS scores. Positive correlations were found with pitch variability and Zero Crossing Rate (ZCR), while negative correlations were found with the speed and number of vocal responses to the clinician, and the overall number of vocalizations. Using these features, we built several Deep Neural Network (DNN) algorithms to estimate ADOS scores and compared their performance with Linear Regression and Support Vector Regression (SVR) models. We found that a Convolutional Neural Network (CNN) yielded the best results. This algorithm predicted ADOS scores with a mean RMSE of 4.65 and a mean correlation of 0.72 with the true ADOS scores when trained and tested on different sub-samples of the available data. Automated algorithms with the ability to predict ASD severity in a reliable and sensitive manner have the potential of revolutionizing early ASD identification, quantification of symptom severity, and assessment of treatment efficacy.

Original languageEnglish
Article number9151117
Pages (from-to)139489-139500
Number of pages12
JournalIEEE Access
StatePublished - 1 Jan 2020


  • Audio signals
  • autism
  • autism diagnostic observation schedule
  • autism spectrum disorder
  • convolutional neural network
  • deep neural network
  • early detection
  • outcome measure
  • pitch
  • speech
  • symptom severity
  • treatment efficacy
  • zero crossing rate

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


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