Enhancing Speech Emotion and Gender Prediction through Advanced RNN Architectures and Comprehensive Dataset Analysis

Speech-based gender and emotion recognition is essential for a number of applications, such as individualized virtual assistants, human-computer interaction, and mental health monitoring. Limited generalization results from traditional machine learning techniques' frequent inability to capture the complex temporal dependencies of voice input. Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) are two Recurrent Neural Network (RNN) architectures that are used in this study’s sophisticated technique to improve predicted accuracy and robustness. One of the main innovations of this study is the combination of thorough dataset analysis to solve issues with noise and data imbalance with feature engineering techniques that extract spectral, prosodic, and temporal properties. This work prioritizes a multi-task learning technique, which optimizes model performance across a variety of datasets, in contrast to traditional approaches. Through rigorous experimentation and comparative analysis, the proposed method demonstrates significant improvements over traditional models, ensuring high adaptability and scalability for real-world deployment.