HPRO: Hierarchical Progressive Reward Optimization via Preference Extraction for Emotional Text-to-Speech
arXiv:2606.28249v1 Announce Type: cross Abstract: Recently, Large Language Model (LLM)-based Text-to-Speech (TTS) models have achieved remarkable naturalness. However, the standard Supervised Fine-Tuning paradigm often converges to statistically averaged prosody, limiting emotional expressiveness. While preference-driven optimization offers a promising alternative, existing approaches suffer from two structural mismatches: information conflict, where content and emotion in a shared latent space produce conflicting gradients, leading to reward hacking and semantic degradation; and scale gap, wh
The rapid advancement of LLMs has brought about a need to overcome limitations in emotional expressiveness for generative AI, making this a timely development in refining human-like AI interaction.
Improving emotional expressiveness in Text-to-Speech models is crucial for more natural human-computer interaction, enhancing the utility and adoption of AI assistants and digital interfaces.
The ability to generate emotionally nuanced speech will advance AI's capacity for empathetic communication and complex human-like interactions, moving beyond statistically averaged prosody.
- · AI developers
- · Customer service platforms
- · Entertainment industry
- · Accessibility technology
- · Monotonous AI voice providers
- · Simple TTS solutions
More natural and engaging AI voice interactions become possible across various applications.
Increased user satisfaction and adoption rates for AI-powered services relying on voice communication.
The development of AI systems capable of sophisticated emotional understanding and response in real-time conversations.
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