EmoStream: Real-time Emotion Prediction through Speech with Apache Flink and Kafka Integration

Advancements in real-time emotion recognition systems have witnessed a surge in interest, fuelled by their potential applications in diverse fields such as human computer interaction, mental health monitoring, and user experience enhancement. Existing approaches often focus on singular modalities, such as speech analysis or facial expressions. However, a comprehensive understanding of human emotion necessitates the integration of multiple modalities. This research introduces a novel approach, a Multimodal Real-time Emotion Prediction System (MREPS), which combines speech analysis with streaming data processing. The system begins by capturing a continuous stream of audio data, which undergoes Speech-to-Text (STT) conversion using cloud-based services like Google Cloud Speech-to-Text. Simultaneously, facial expression recognition and physiological signal analysis can be integrated for a more comprehensive emotional profile. The real-time nature of the data necessitates the use of a robust streaming architecture, leveraging technologies such as Apache Kafka for data ingestion and Apache Flink for stream processing. A crucial component of the system is the Emotion Prediction API, a microservice housing a machine learning model for emotion classification. This model is trained on diverse datasets to ensure accuracy across various languages, cultures, and individual differences. The real-time API endpoint continuously processes the multimodal input, providing dynamic emotion predictions.