This “paper investigated the signal processing and feature extraction of the aforementioned speeches in order to investigate the intelligent machine technology for comprehending infants' needs and emotions from speech signals and, as a result, assisting parents in child rearing. It would appear that the infant's speech signals alone were not sufficient to achieve a high level of precision and dependability when it came to dealing with a variety of needs and emotions. Taking into account the combined characteristics of acoustic characteristics and rearing behaviors, an efficient recognition strategy was therefore proposed using the SVM classification algorithm. The findings of the experiment demonstrated that the majority of infants' typical physiological and psychological states, such as happiness, hunger, and slumber, can be correctly identified with a relatively high level of accuracy.
SVM classification algorithm, digital processing, Matlab, MFCS, Speech signal
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