Hybrid Lyrebird Red Panda Optimization_Shepard Convolutional Neural Network for recognition of speech emotion in audio signals

Speech serves as the primary mode of human communication, where semantic meaning is conveyed through the combination and arrangement of words. Recent research in Speech Emotion Recognition (SER) has assisted in maintaining and improving social relationships and behaviors among individuals. In recent times, several advancements have been attained in SER systems due to the incorporation of Deep learning (DL) models. However, the conventional techniques often require large, well-annotated datasets for effective training, which was resource-intensive to collect and label. Moreover, models may struggle to generalize across diverse speakers, emotional expressions, and recording conditions, potentially limiting their real-world applicability. Therefore, this paper presents a Lyrebird Red Panda Optimization _Shepard Convolutional Neural Network (LRPO_ShCNN) for SER. Initially, the input speech signal is preprocessed by using Adaptive Gaussian filter. After that, the significant features from the preprocessed image are extracted. Further, data augmentation process is carried out and it generates new data points from existing data. After that, feature selection is done with LRPO. Finally, SER is accomplished by utilizing ShCNN, where the emotions are classified. Moreover, the hyperparameters of ShCNN are tuned with LRPO, which is developed by the integration of Lyrebird Optimization Algorithm (LOA) and Red Panda Optimization (RPO). The evaluation results shows that the LRPO_ShCNN obtained Accuracy, Positive Predictive Value (PPV), Negative Predictive Value (NPV), True Positive Rate (TPR), and True Negative Rate (TNR) as 91.092 %, 90.552 %, 90.876 %, 91.230 %, and 91.818 % respectively.