Analysis of emotion in autism spectrum disorder children using Manta-ray foraging optimization

•Understanding emotions conveyed through human voices is crucial, particularly in discerning behavioral and emotional traits.•Effective recognition of emotions from voice data demands proficient feature extraction supported by adept feature selection and optimization techniques.•This study focuses on exploring emotion comprehension through voice analysis by integrating multiple feature extraction methods.•Specifically targeting emotions in children diagnosed with Autism Spectrum Disorder (ASD), this research combines Mel Frequency Cepstral Coefficients (MFCC), Linear Prediction Coefficients (LPC), and Gammatone Filter Cepstral Coefficient (GFCC) for feature extraction. Notably, it introduces the novel application of the Manta Ray Foraging Optimization (MRFO) algorithm for emotion analysis in ASD children's voices.•This combination of multiple feature extraction methods along with the manta ray optimization algorithm in the voice-based analysis of emotions of ASD children has contributed to the state-of-the-art result in this research field.•Additionally, the approach identifies the ASD children's key emotions and the next likely perception of emotions, enabling them to analyze their emotional state. Understanding emotions conveyed through human voices is crucial, particularly in discerning behavioral and emotional traits. Effective recognition of emotions from voice data demands proficient feature extraction supported by adept feature selection and optimization techniques. This study focuses on exploring emotion comprehension through voice analysis by integrating multiple feature extraction methods. Specifically targeting emotions in children diagnosed with Autism Spectrum Disorder (ASD), this research combines Mel Frequency Cepstral Coefficients (MFCC), Linear Prediction Coefficients (LPC), and Gammatone Filter Cepstral Coefficient (GFCC) for feature extraction. Notably, it introduces the novel application of the Manta Ray Foraging Optimization (MRFO) algorithm for emotion analysis in ASD children's voices. This combination of multiple feature extraction methods along with the manta ray optimization algorithm in the voice-based analysis of emotions of ASD children has contributed to the state-of-the-art result in this research field. Additionally, the approach identifies the ASD children's key emotions and the next likely perception of emotions, enabling them to analyze their emotional state. The experimental research reveals that the developed system per