Advancements in Bangla Speech Emotion Recognition: A Deep Learning Approach with Cross-Lingual Validation

Speech Emotion Recognition (SER) is a method where computers learn to recognize human emotions from speech to improve communication. In this study, we present an innovative Bangla SER framework, incorporating data augmentations, feature extractions, and a deep learning model. We utilize a publicly available Bangla SER dataset named SUBESCO, developed by Shahjalal University of Science and Technology (SUST), which stands as one of the largest datasets in the Bangla SER domain. To enhance the data's amount and variety, we use noise injection, time stretching, time shifting, and pitch augmentation. Previous Bangla SER models have struggled with severe misclassification in lower tonal classes, highlighting the need for more robust feature extraction. Therefore, we have conducted feature extraction, including Zero Crossing Rate (ZCR), Chroma stft, Mel-frequency Cepstrum Coefficient (MFCC), Root Mean Square, and Mel Spectrogram, to improve classification accuracy. This paper aims to use a Deep Neural Network (DNN) for recognizing emotion labels from Bangla spoken speech data and has achieved 99% accuracy on unseen data. Given the scarcity of Bangla SER datasets and to validate the model's robustness, we tested its performance on additional lingual datasets, namely RAVDESS, TESS, EMOVO, EmoDB, and BanglaSER. Remarkably, our model demonstrated high accuracy rates of 90%, 99%, 96%, 95%, and 93% on these datasets, respectively.