New Framework Enables AI Systems to Plan and Express Emotions in Real-Time Speech
Researchers have developed Self-EmoQ, a framework that allows conversational AI systems to determine appropriate emotions before generating text, then synthesize that emotional content into streaming speech. The system uses reinforcement learning trained on Plutchik's wheel of emotions theory and was tested on multiple dialogue datasets. This advancement could make AI interactions feel more natural and emotionally coherent for users.
Self-EmoQ is an emotion-planning framework designed to address a gap in current conversational AI: the lack of self-emotion determination mechanisms that drive emotional text-to-speech synthesis. The system works by determining an emotion prior to text generation, then grounding downstream emotional TTS in a streaming manner. Implemented as a plug-and-play module built from pretrained large language models, the framework is trained using reinforcement learning where emotions function as actions. The approach combines imitation learning signals with theory-driven scoring based on Plutchik's wheel of emotions. Experiments across four dialogue datasets (DailyDialog, EmoryNLP, IEMOCAP, and MELD) show the method outperforms prompting and finetuning baselines on both emotion determination and response quality. The researchers have implemented a complete streaming pipeline for real-time deployment, with speech quality assessments confirming emotional alignment, contextual coherence, and expressive fluency.
What's missing
The paper does not discuss potential limitations or failure cases of the emotion-planning approach, nor does it address how the system handles culturally-specific emotional expressions or edge cases where emotion determination may be ambiguous or context-dependent.
What different sources said
- arXiv cs.AICenter
Self-EmoQ: Plutchik-Guided Value-based Planning to Drive Streaming Emotional TTS
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