Deep Learning-Based Emotion Recognition for Better Human-Computer Interaction

Abstract

In order for systems to comprehend and respond more appropriately to user emotions, emotion recognition from speech is an essential component in enhancing human-computer interaction (HCI). An in-depth examination of deep learning methods for voice emotion recognition, focusing on convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid designs that integrate feature extraction and temporal analysis. We examine the description of features using spectrograms and mel-frequency cepstral coefficients (MFCCs), as well as the impact of acoustic features on the model's ability to classify emotions thru pitch, tone, and energy. We evaluate various deep learning algorithms that utilize large datasets and robust model designs to accurately identify emotions such as happiness, sadness, anger, and neutrality. Cultural differences, speaker unpredictability, and ambient noise are among the challenges to emotion perception that this study addresses. understanding how to utilize deep learning to create emotion recognition systems that are far more accurate and responsive, and how to design human-computer interfaces that are more empathetic and responsive to users' needs.