Harnessing Deep Learning for Robust Speech Emotion Recognition: A Multimodal Approach

Abstract

Speech Emotion Recognition (SER) represents a cutting-edge field that combines elements of artificial intelligence and human-computer interaction. This research domain focuses on developing systems capable of accurately identifying and interpreting human emotions from speech signals. The paper in question delves into the application of advanced deep learning methodologies in conjunction with both acoustic and linguistic features to enhance the performance and reliability of SER models. The study conducts a comprehensive evaluation of diverse neural network structures, including but not limited to convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention-based models. These architectures are examined for their efficacy in capturing the nuanced emotional cues present in speech data. Additionally, the research investigates various feature extraction techniques, exploring both traditional acoustic features such as pitch, energy, and spectral characteristics, as well as more sophisticated linguistic features derived from natural language processing. A key aspect of this work is the emphasis on multimodal learning, which involves the integration of multiple data modalities - in this case, acoustic and linguistic information. This approach aims to leverage the complementary nature of these different data types, potentially leading to more robust and accurate emotion recognition models. The comparative analysis presented in the paper likely highlights the strengths and limitations of different methodologies, providing valuable insights for researchers and practitioners in the field of affective computing and speech processing. By exploring the synergy between deep learning and multi- modal feature analysis, this research contributes to the ongoing efforts to create more sophisticated and human-like emotion recognition systems. Such advancements have far-reaching im- plications for various applications, including virtual assistants, healthcare monitoring, and interactive educational systems.