Recognition of Emotion Behind Speech Using Deep Learning RESNET Algorithm

This work aims to develop a reliable and efficient model that utilizes machine learning techniques to classify speech with high accuracy. The model is built using the Python programming framework Librosa, which extracts tonal information from audio and modifies it using the Fourier transform to plot patterns onto graphs. The system uses deep learning algorithms from ResNet to classify speech, achieving an accuracy range of 85–90%. The model is tested with random inputs to evaluate its functionality, response generation, and potential crashes. The proposed system is based on patterns generated by analyzing collected samples and is designed to produce accurate and error-free results. The system combines the starting model with seed neural network layers and dropout layers to improve performance and uses RELU activation in the hidden layer. The method has a far higher accuracy than previous models (between 60% and 75%).