Multitask Learning for Emotion and Gender Recognition in Arabic Speech Using CNN-LSTM Architectures

This paper proposes a multitask learning framework for simultaneous emotion and gender recognition from Arabic speech. Leveraging a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture, the model captures both spectral and temporal characteristics of speech. By using Mel Frequency Cepstral Coefficients (MFCCs) as features, the model achieves an emotion classification accuracy of 86.05% and a gender classification accuracy of 97.80%. This approach provides insights into the dynamic interplay between gender and emotion in speech, highlighting how gender influences emotion recognition and vice versa. The results suggest that combining CNN-LSTM architectures for emotion and gender classification can improve performance over traditional methods, particularly for languages with underrepresented datasets like Arabic. The paper contributes a unique dataset and demonstrates the effectiveness of multitask learning in addressing the challenges of emotion and gender recognition in speech.