SPEECH EMOTION RECOGNITION USING COMBINED TRIPLET AND SINGLE-HEADED ATTENTION-ENABLED INTEGRATED RECURRENT NETWORK

Speech Emotion Recognition (SER) stands as a crucial field within Human–Computer Interaction (HCI), drawing significant attention from researchers because of its broad applications. The ability to recognize emotions conveyed through speech is essential for effective two-way communication, making it a pivotal aspect in various domains. Several existing researches have addressed certain drawbacks such as dataset issues, lack of computational efficiency, interpretability, and so on. To overcome the issues and to perform the efficient SER, a combined Triplet and Single-headed Attention-enabled Recurrent Long Gated Network (TriSiH-RLG2) model is proposed in the research, which is designed through the integration of triplet attention, single-headed attention, and recurrent networks that includes Recurrent Neural Network (RNN), Long Short-term Memory (LSTM), and Gated Recurrent Unit (GRU), which eliminates the drawbacks of each model and enhances the overall efficacy of the research. Further to elevate the accuracy of the research, the Gradient Boosting Machine (GBM) is utilized as the classifier that performs the efficient SER through the ensemble learning approach. The contributed TriSiH attention is the incorporation of the two standard attention mechanisms, which act as the spotlight to highlight the relevant features from the input, thus making the research model highly proficient. The experimentation is done using RAVDESS emotional speech dataset and the performance of the provided model is revealed through metrics such as Precision, Recall, Accuracy, and [Formula: see text] 1-score that achieve 94.338%, 98.344%, 96.875%, and 96.299%, respectively.