Multimodal Hidden Markov Models for Persistent Emotional State Tracking

1. 1Build a multimodal hidden Markov model to track emotional states in conversations
2. 2Run experiments to evaluate the model's performance on utterance-level sentiment analysis
3. 3Configure the model to incorporate multiple modalities, such as text, audio, and video
4. 4Test the model on clinical conversation datasets to assess its accuracy and robustness
5. 5Apply the model to real-world conversational systems to improve emotion recognition and guidance

Data scientists and AI engineers working on conversational AI or emotion recognition systems can benefit from this research to improve their models' accuracy and interpretability

💡 Multimodal hidden Markov models can effectively track persistent emotional states in conversations, improving emotion recognition and interpretability

💡 Track emotional states in conversations with multimodal hidden Markov models! 🤖💬 #emotionrecognition #conversationalAI