Although current neural text-to-speech (TTS) models are able to generate high-quality speech, intensity controllable emotional TTS is still a challenging task. Most existing methods need external optimizations for intensity calculation, leading to suboptimal results or degraded quality. In this paper, we propose EmoDiff, a diffusion-based TTS model where emotion intensity can be manipulated by a proposed soft-label guidance technique derived from classifier guidance. Specifically, instead of being guided with a one-hot vector for the specified emotion, EmoDiff is guided with a soft label where the value of the specified emotion and \textit{Neutral} is set to $\alpha$ and $1-\alpha$ respectively. The $\alpha$ here represents the emotion intensity and can be chosen from 0 to 1. Our experiments show that EmoDiff can precisely control the emotion intensity while maintaining high voice quality. Moreover, diverse speech with specified emotion intensity can be generated by sampling in the reverse denoising process.

翻译：尽管当前神经文本转语音（TTS）模型能够生成高质量语音，但强度可控的情感TTS仍是一项具有挑战性的任务。现有方法大多需要外部优化来计算情感强度，导致结果次优或质量下降。本文提出EmoDiff——一种基于扩散的TTS模型，通过引入源于分类器引导的软标签引导技术，实现情感强度的操控。具体而言，EmoDiff不再使用指定情感的独热向量进行引导，而是采用软标签引导，其中指定情感与"中性"情感的值分别设置为$\alpha$与$1-\alpha$。这里的$\alpha$代表情感强度，取值范围为0到1。实验表明，EmoDiff能够精确控制情感强度，同时保持高语音质量。此外，通过反向去噪过程中的采样，可生成具有指定情感强度的多样化语音。