A Novel Approach to Speech Emotion Recognition Using Wav2Vec2

Speech Emotion Recognition (SER), which allows computers to precisely understand and react to human emotions, is essential for improving human-computer interaction. This subject is crucial because it has the potential to significantly improve the user experience in applications including education and e-Iearning, mental health, customer service, Automotive Industry, and others. Speech Emotion Recognition (SER) systems that used classic machine learning techniques usually extract handcrafted audio features such as pitch, loudness, energy, spectral flux, and others each of which captured a distinct part of the emotional tone in speech. Despite their ease of use and interpretability, traditional machine learning models such as Support Vector Machines (SVMs) and Decision Trees frequently had trouble handling complex emotional patterns and speaker variances. These limitations led researchers to use deep learning models, specifically Recurrent Neural Networks (RNNs), which could analyze speech as a sequence and capture some temporal relationships. By simulating the evolution of emotions over time, RNN s improved emotion identification. However, RNNs faced their own limitations, particularly in handling long-range dependencies and in maintaining performance when faced with diverse emotional expressions or noisy environments. These challenges led to the Transformer models. Unlike previous models, Transformers can capture long-range dependencies and complex patterns in data, making them well-suited for capturing the nuanced expressions necessary for accurate emotion detection. Therefore, in this research, we will present a Wav2Vec2 transformer SER system that has been optimized on emotional speech data to recognize a variety of emotional states. We conducted our experiment on the small dataset called Ryerson Audio-Visual Database of Emotional Speech and Song (RA VDESS) and achieved an improvement in accuracy and robustness over previous methods. It achieved a testing accuracy of 89% when compared to SVM using GWO optimizer, which achieved an accuracy of 76%, and the LSTM using GWO optimizer, which achieved an accuracy of 71 %.