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Expressive Prompting: Improving Emotion Intensity and Speaker Consistency in Zero-Shot TTS
arXiv – CS AI|Haoyu Wang, Chunyu Qiang, Tianrui Wang, Cheng Gong, Yu Jiang, Yuheng Lu, Chen Zhang, Longbiao Wang, Jianwu Dang|
🤖AI Summary
Researchers developed a two-stage prompt selection strategy for zero-shot text-to-speech synthesis that improves emotional intensity and speaker consistency. The method evaluates prompts using prosodic features, audio quality, and text-emotion coherence in a static stage, then uses textual similarity for dynamic prompt selection during synthesis.
Key Takeaways
- →New two-stage prompt selection strategy addresses limitations in existing zero-shot TTS systems for expressive speech synthesis.
- →Static evaluation stage uses pitch-based prosodic features, perceptual audio quality, and LLM-assessed text-emotion coherence scores.
- →Dynamic stage employs textual similarity models to select prompts most aligned with input text during synthesis.
- →Method demonstrates improved high-intensity emotional expression and robust speaker identity in synthesized speech.
- →Approach specifically targets the challenge of ensuring stable speaker cues and appropriate emotional intensity in AI speech generation.
#text-to-speech#zero-shot#speech-synthesis#emotion-ai#llm#audio-generation#prompt-engineering#expressive-ai
Read Original →via arXiv – CS AI
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