Deep Learning Models for Emotion Classification in Audio Signals

This project aims to build machines that can communicate with people via voice, which is a particularly active field of study in artificial intelligence and machine learning. Speech is a rich source of information that can carry paralinguistic information in addition to linguistic information. Emotion is one such important example of this kind of paralinguistic information that is partly expressed through speech. Paralinguistic information, i.e., emotion, is enabled through the development of machines. Human-machine communication becomes easier and natural with such machines. In this study, We investigated how well Convolutional Neural Networks (CNNs) could identify emotions in speech. To achieve this, we used broad-band spectrograms of speech signals as input features. The speech samples were collected from actors who deliberately expressed specific emotions while speaking. To enhance the model's performance and generalization, we trained and tested our models on speech datasets in multiple languages. Additionally, we applied two levels of data augmentation to enrich the training data, ensuring improved robustness and accuracy in emotion recognition. The dropout method was used to regularize the networks. We can use Convolutional neural network with MFCC in this project to identify the speech signals. Experimental results can be applied in Python framework and provide better accuracy in classification.