Abstract:
This project introduces a Speech Emotion Detection System implemented in Python, leveraging machine learning and signal processing techniques to discern and analyze emotional states expressed in spoken language. The system aims to enhance human-machine interaction by providing valuable insights into users’ emotional responses, with applications spanning from customer service to mental health monitoring.
The Speech Emotion Detection System utilizes Python’s libraries for audio processing, such as Librosa, to extract relevant features from speech signals. Machine learning models, including deep neural networks or ensemble classifiers, are trained on diverse emotion-labeled datasets to learn patterns associated with different emotional states.
Key features of the system include real-time emotion recognition, support for multiple emotion classes (e.g., joy, anger, sadness), and the ability to process various audio input formats. The system can be integrated into voice-enabled applications, enhancing user experience by adapting responses based on detected emotions.
The open-source nature of the implementation encourages collaboration and customization, allowing developers to adapt the system to specific applications or domains. The research evaluates the system’s performance through rigorous testing on diverse datasets, assessing its accuracy in recognizing a wide range of emotional expressions.
By providing a Speech Emotion Detection System in Python, this project contributes to the growing field of affective computing, enriching human-machine interactions with the ability to perceive and respond to users’ emotional states. The system stands as a versatile tool with applications in industries such as customer service, virtual assistants, and mental health support, fostering a more empathetic and responsive interaction between users and technology.