Bridging the Emotional Gap in AI: A Study on Speech Emotion Recognition for Adaptive Human Computer Interaction

Speech Emotion Recognition (SER) is an advancement that has attracted a lot of interest because of its potential uses in intelligent systems, mental health monitoring, and human-computer interaction (HCI). Even with the progress made in AI-driven HCI, many systems are still unable to accurately sense and comprehend human emotions. Virtual assistants may carry out tasks based on spoken instructions, but they don't react well to user’s emotions, which results in less-than-ideal interactions. In order to close this gap, this study implements a speech emotion recognition algorithm that uses the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), and Toronto Emotional Speech Set (TESS) datasets to examine voice characteristics. The system makes use of cutting-edge deep learning methods including Long Short-Term Memory (LSTM) networks to capture temporal relationships in speech patterns and Convolutional Neural Networks (CNNs) for feature extraction from spectrograms which results in building a Hybrid Model. Traditional machine learning models may not be able to capture subtle emotional nuances in complex data, but they do offer faster processing times and easier implementations. Conversely, deep learning models, including 2D architecture such as CNNs and LSTMs, need more processing power but are better at handling large datasets and detecting subtle emotional cues. By leveraging these advancements, this research aims to enhance virtual assistants and similar systems to better recognize and respond to emotional cues in real time. Thus, Opening the door for a technological environment that is more user-centered and sympathetic.