New Method Improves Emotional Expression in AI Text-to-Speech Systems
Researchers have developed a technique called Cross-modal Consistency Guided Classifier-Free Guidance (CCG-CFG) that improves how AI text-to-speech systems convey emotions when instructed to do so. The method addresses a key problem: when the requested emotion conflicts with the text's natural meaning, speech quality typically suffers. The advancement could lead to more natural and expressive AI-generated speech across applications like virtual assistants and audiobook narration.
A research team has introduced CCG-CFG, a new approach for enhancing emotional control in auto-regressive text-to-speech (TTS) models. The method works by measuring inconsistencies between the text's semantic emotion and the target speech emotion, then dynamically adjusting guidance signals accordingly. The researchers also employed a hard-sample mining strategy to better train the system on difficult cases. Testing on five emotional speech datasets and two TTS benchmarks showed the technique improved emotion-recognition accuracy by up to 12% and subjective quality scores by 10% when applied to the CosyVoice2 system, while maintaining speech intelligibility and naturalness. The approach outperformed comparable systems including HierSpeech++ and Qwen3-TTS.
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- arXiv cs.CLCenter
Cross-modal Consistency Guidance for Robust Emotion Control in Auto-Regressive TTS Models
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