Emotion Recognition From Speech Using Wavelet Packet Transform Cochlear Filter Bank and Random Forest Classifier

This research aims to design and implement an artificial emotional intelligence system that is capable of identifying the unknown emotion of the speaker. To that end, we propose a novel framework for emotion recognition in the presence of noise and interference. Our approach accounts for energy, time and spectral parameters to examine the emotion of the speaker. However, rather than using Gammatone filterbank and short-time Fourier transform (STFT), commonly adopted in the literature, we propose employing a novel wavelet packet transform (WPT) based cochlear filterbank. Our system, coupling this representation with random forest classifier, shows superior performance over other existing algorithms when appraised on three distinct speech corpora in two different languages, and considering also stressful and noisy talking conditions.