Ameya Ajit Mande, Mechanical Engineering Department Maharashtra Institute of Technology Aurangabad
2019 International Journal of Advanced Research in Computer Science  
A person's speech can be altered by various changes in the autonomic nervous system and effective technologies can process this information to recognize emotion. As an example, speech produced in a state of fear, anger, or joy becomes loud and fast, with a higher and wider range in pitch, whereas emotions such as sadness or tiredness generate slow and low-pitched speech. Detection of human emotions through voice-pattern and speech-pattern analysis has many applications such as better assisting
more » ... uman-machine interactions. This paper aims to detect emotions from audio. Several machine learning algorithms including K-nearest neighbours (KNN) and decision trees were implemented, based on acoustic features such as Mel Frequency Cepstral Coefficient (MFCC). Our evaluation shows that the proposed approach yields accuracies of 98%, 92% and 99% using KNN, Decision Trees and Extra-Tree Classifiers, respectively, for 7 emotions using Toronto Emotional Speech Set (TESS) Dataset.
doi:10.26483/ijarcs.v10i6.6489 fatcat:pbc5qb5c3ff4vj4k75mqbam4hm