EmoShift: 轻量级激活引导用于增强情感感知语音合成
EmoShift: Lightweight Activation Steering for Enhanced Emotion-Aware Speech Synthesis
AI总结：
EmoShift通过轻量级激活引导框架提升情感感知语音合成的情感表达与自然性。
摘要：
实现精确且可控的情感表达对于生成自然且适合上下文的语音在文本到语音（TTS）合成中至关重要。然而，许多情感感知TTS系统，包括基于大型语言模型（LLM）的设计，依赖于缩放固定情感嵌入或外部指导，限制了它们对情感特定潜在特征的建模能力。为了解决这一差距，我们提出了EmoShift，一种轻量级的激活引导框架，其中包含一个EmoSteer层，该层为每个目标情感在输出嵌入空间中学习一个引导向量，以捕捉其潜在偏移，并在语句和类别之间保持稳定且适当的表现。EmoShift仅有1000万可训练参数，不到全微调的1/30，其在客观和主观评估中优于零样本和全微调基线，增强了情感表达性的同时保持自然性和说话人相似性。进一步分析证实了所提出的EmoSteer层的有效性，并揭示了其在语音合成中可控情感强度的潜力。
英文摘要：
Achieving precise and controllable emotional expression is crucial for producing natural and context-appropriate speech in text-to-speech (TTS) synthesis. However, many emotion-aware TTS systems, including large language model (LLM)-based designs, rely on scaling fixed emotion embeddings or external guidance, limiting their ability to model emotion-specific latent characteristics. To address this gap, we present EmoShift, a lightweight activation-steering framework incorporating a EmoSteer layer, which learns a steering vector for each target emotion in the output embedding space to capture its latent offset and maintain stable, appropriate expression across utterances and categories. With only 10M trainable parameters,less than 1/30 of full fine-tuning, EmoShift outperforms zero-shot and fully fine-tuned baselines in objective and subjective evaluations, enhancing emotional expressiveness while preserving naturalness and speaker similarity. Further analysis confirms the proposed EmoSteer layer's effectiveness and reveals its potential for controllable emotional intensity in speech synthesis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
