Cross-modal Consistency Guidance for Robust Emotion Control in Auto-Regressive TTS Models
Researchers have developed a new method called Cross-modal Consistency Guided Classifier-Free Guidance (CCG-CFG) to improve emotion control in auto-regressive Text-to-Speech (TTS) models. This technique dynamically adjusts guidance scales based on the conflict between textual and desired speech emotions, enhancing emotional alignment. When applied to the CosyVoice2 model, this approach led to significant improvements in emotion recognition accuracy and subjective quality scores, outperforming existing methods like HierSpeech++ and Qwen3-TTS. AI
IMPACT Enhances TTS expressiveness and accuracy, potentially leading to more natural and emotionally resonant AI-generated speech.