arXiv:2510.13293v4 Announce Type: replace Abstract: While Text-to-Speech (TTS) systems enable emotional control via natural-language instructions, expressiveness, naturalness, and speech quality degrade when the target emotion conflicts with the textual semantics. We propose a Cross-modal Consistency Guided Classifier-Free Guidance (CCG-CFG) method with dynamic scales based on the degree of inconsistency between the text emotion and the explicit speech emotion, replacing the dropout condition with the text emotion. We also distill the CCG-CFG guidance signal using a hard-sample mining strategy
This research addresses a current limitation in emotional AI, specifically the degradation of expressiveness in TTS when target emotion conflicts with textual semantics, indicating active development in refining AI speech generation.
Improved emotional control in TTS models enhances the realism and utility of voice AI across various applications, from customer service to entertainment, making AI interactions more natural and effective.
The ability to achieve robust emotion control in auto-regressive TTS models, even with conflicting semantic and emotional cues, marks a significant step towards more sophisticated and reliable AI-driven voice synthesis.
- · AI voice synthesis developers
- · Customer service industries
- · Entertainment media (e.g., gaming, virtual assistants)
- · Enterprises adopting advanced AI communication tools
- · TTS models with poor emotional robustness
- · Solutions relying on static or less nuanced voice generation
Enhanced human-computer interaction through more emotionally intelligent voice interfaces will become more commonplace.
Increased user adoption and reliance on AI-driven voice applications for daily tasks and information consumption.
Potential blurring of lines between human and AI speech, raising new ethical considerations regarding authenticity and manipulation.
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