Recognition of emotions in speech using deep CNN and RESNET

The acts we engage in that transmit our emotional state or attitude to other people are referred to as emotional expressions. Communication, both verbal and nonverbal, is how they manifest themselves in the world. One of the most difficult problems to solve in data science is the problem of voice emotion recognition, often known as categorisation. In this study, we used two independent datasets, referred to as RAVDESS and TESS, each including seven distinct feelings, including neutral, happy, sad, angry, afraid, disgusted, and startled. In the raw audio wave, noise, stretching, shifting, and pitching have been used to perform the preprocessing and data augmentation that have been conducted. The characteristics like MFCC, MFC, and Chroma are taken out of the image. CNN and RESNET are the names of the two models that have been put up as possibilities. In order to achieve a higher level of accuracy in our classifications, we make use of an incremental approach to adjust the pre-trained model. In contrast to some earlier methods, none of the presented models require the data to be converted into a visual representation in order to function. Instead, they all work directly with the raw sound data. According to the findings of our experiments, our best-performing model performs better than any of the existing frameworks for TESS, thereby establishing a new standard for the industry.