A Speech Emotion Recognition System for Improved Communication and Enhancing Human-Machine Interaction

In today's digital world of remote communication, speech remains one of the most natural ways for human beings to express themselves. However, effective communication involves more than just words; emotions playa crucial role in conveying meaning and intent. Therefore, the detection and analysis of emotions in speech have become essential for understanding and improving communication in various applications. Emotion detection is a complex task due to the subjective nature of emotions. To address this challenge, this paper presents a Speech Emotion Recognition (SER) system, which comprises a set of methodologies designed to process and classify speech signals with the goal of detecting underlying emotions. To build and evaluate the SER system, we utilize the RA VDESS and TESS dataset, which contains recorded speech data with annotated emotions. The first step involves augmenting the audio data to enhance its diversity and robustness. Subsequently, we employ feature extraction methods and pre-processing techniques to derive meaningful acoustic features from the audio recordings. The processed data is then fed into machine learning models to perform emotion classification. These models are trained on the extracted features and learn to recognize patterns associated with different emotions. The system can be employed in call center environments to analyze caller-agent conversations, helping businesses gauge customer satisfaction and improve service quality. Additionally, it can find applications in improving virtual voice-based assistants, enabling them to understand human emotions and respond accordingly. Overall, this research aims to advance emotion detection in speech and contribute to the development of more emotionally intelligent communication technologies that can enhance human-machine interactions andre mote conversations.