Identification of Emotions in a Given Speech Audio Clip by Using ANN

Understanding and decoding human emotions from speech audio is a compelling challenge in the field of affective computing. This article focuses on the functioning of Artificial Neural Networks (ANN) to the task of identifying emotions within speech audio clips. The ability to accurately recognize emotions in speech has broad implications, from enhancing human computer interaction to providing invaluable insights into mental health assessments. The research methodology involves collecting a diverse dataset of speech audio clips, each carefully annotated with specific emotional states, like joy, sadness, anger, surprise, and neutrality. Relevant acoustic structures counting pitch, intensity, spectral characteristics, and rhythm patterns, are extracted from these audio clips, thereby creating a feature-rich dataset for ANN training. ANNs are chosen as the primary model based on machine learning concepts for this task. The training process involves iterative adjustments of the ANN's internal parameters to reduce the disparity between predicted emotions and the ground truth labels. Various architectures, such as feedforward networks or more advanced models like recurrent neural networks (RNNs) and convolutional neural networks (CNNs) are explored to identify the effective approach. To determine the performance of the trained ANN, a separate test dataset is used, and standard evaluation metrics including recall, precision, accuracy and confusion matrices are employed. The results proven useful perceptions into the ANN's ability to accurately identify emotions in a given speech audio clips.