arXiv:2606.09837v1 Announce Type: cross Abstract: Emotional interaction is increasingly crucial for conversational AI, yet current systems lack a self-emotion determination mechanism to drive the streaming text-to-speech (TTS) synthesis. We propose an emotion-planning framework that determines the emotion prior to the textual generation, grounding the downstream emotional TTS in a streaming manner. The framework is implemented by a plug-and-play LLM module, initialized from pretrained LLMs, and trained by reinforcement learning (RL) with emotions as the actions. A hybrid reward is employed whi
The increasing sophistication of conversational AI and the demand for more human-like interaction are driving innovation in emotional synthesis for TTS.
This development represents a step towards more natural and contextually aware AI interactions, fundamentally changing how humans perceive and engage with AI systems.
AI systems will be able to determine and express emotions prior to textual generation, leading to more coherent and emotionally aligned streaming text-to-speech outputs.
- · Conversational AI developers
- · Customer service industries
- · Entertainment media
- · Educational technology
- · AI systems with robotic, emotionless voices
- · Companies unable to integrate advanced emotional AI
- · Voice actors for generic roles
Emotional nuance in AI-generated speech dramatically improves human-AI communication and engagement.
The ability of AI to self-determine and express emotion will accelerate the development of more empathetic and persuasive AI agents.
This could lead to ethical dilemmas surrounding AI manipulation through emotional expression, necessitating new regulatory frameworks.
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Read at arXiv cs.AI