Exploring the Differences between Deaf and Hearing Infant Cries

Abstract

In this study, we propose an interpretable AI approach for analyzing infant cry signals, focusing on distinguishing between deaf and hearing infants. Using the Baby Chillanto dataset, we first conduct a human test to determine how well humans can detect the differences between hearing and deaf infants. We then explore the use of typical features used in audio signal analysis - chromagram, Log-Mel spectrogram and Mel frequency cepstral coefficients (MFCC), to determine whether a deep learning model can perform as well or better than humans. To explain the model’s predictions, we apply SHapley Additive exPlanations(SHAP) which reveal the most significant portion of each feature set that contributes to the model’s decisions. This interpretable approach provides valuable insights into how acoustic characteristics differ between deaf and hearing infants, potentially aiding early detection and understanding of hearing impairments through non-invasive cry signal analysis. While the combined human test results were near random, our deep learning approach demonstrated both high classification performance and enhanced explainability.