Automated Speech Recognition System to Detect Babies’ Feelings through Feature Analysis

Diagnosing a baby’s feelings poses a challenge for both doctors and parents because babies cannot explain their feelings through expression or speech. Understanding the emotions of babies and their associated expressions during different sensations such as hunger, pain, etc., is a complicated task. In infancy, all communication and feelings are propagated through cry-speech, which is a natural phenomenon. Several clinical methods can be used to diagnose a baby’s diseases, but nonclinical methods of diagnosing a baby’s feelings are lacking. As such, in this study, we aimed to identify babies’ feelings and emotions through their cry using a nonclinical method. Changes in the cry sound can be identified using our method and used to assess the baby’s feelings. We considered the frequency of the cries from the energy of the sound. The feelings represented by the infant’s cry are judged to represent certain sensations expressed by the child using the optimal frequency of the recognition of a real-world audio sound. We used machine learning and artificial intelligence to distinguish cry tones in real time through feature analysis. The experimental group consisted of 50% each male and female babies, and we determined the relevancy of the results against different parameters. This application produced real-time results after recognizing a child’s cry sounds. The novelty of our work is that we, for the first time, successfully derived the feelings of young children through the cry-speech of the child, showing promise for end-user applications.