# ECE-TTS: A Zero-Shot Emotion Text-to-Speech Model with Simplified and Precise Control
## 2 Ece-Tts
### Emotion Control Strategy
#### 2.3.2 Emotion Intensity Modulation via Emotion Arithmetic  
From Figure 4, it can be observed that speech samples of different emotions exhibit distinct distribution patterns. Therefore, we infer that the Arousal (A), Valence (V), and Dominance (D) values of an audio sample can effectively reflect changes in emotion intensity. Each emotion occupies a characteristic range within these three dimensions, and as emotion intensity changes, the corresponding V, A, and D values adjust accordingly. To achieve precise and flexible control over emotion intensity, this work utilizes an emotion arithmetic-based approach [46]. The core idea of this approach is to build a speech synthesis system that can precisely control emotion intensity by combining pretraining and fine-tuning. Formally, let \(\theta_{pre}\in\mathbb{R}^{d}\) denote the weights of the pretrained model, and let \(\theta_{e}\in R^{d}\) represent the weights of the fine-tuned model for a specific emotion \(e\). The emotion vector \(\phi_{e}\in\mathbb{R}^{d}\) is then computed element-wise as:  
\[\phi_{e}=\theta_{e}-\theta_{\text{pre}}, \tag{17}\]  
Figure 4: Visualization of 300 speech samples from ground truth recordings across neutral, happy, angry, and sad categories, showing their Valence (V), Arousal (A), and Dominance (D) values. These serve as a reference baseline for analyzing the variation trends. To adjust emotion intensity, the emotion vector \(\phi_{e}\) is incorporated into the base model \(\theta\) through element-wise addition, controlled by a scaling factor \(\alpha\):  
\[\theta_{\text{new}}=\theta+\alpha\phi_{e}, \tag{18}\]  
By modifying \(\alpha\), the model can enhance or attenuate the emotion intensity in synthesized speech. Specifically, when \(\alpha=1\), the new model \(\theta_{\text{new}}\) becomes equivalent to the fine-tuned model for emotion \(e\), the generated speech exhibits the exact emotional characteristics of the fine-tuned model for emotion \(e\). Conversely, when \(\alpha<1\), the intensity of the emotional expression is gradually reduced, and for \(\alpha>1\), the emotional attributes are amplified. To ensure robust emotion intensity control, the model is initially pretrained on our emotional speech dataset, obtaining a generalized set of pretrained weights \(\theta_{pre}\). This pretraining process covers multiple emotion types, such as happiness, sadness, and anger, allowing the model to capture a broad range of emotionally expressive speech features. After pretraining, fine-tuning is conducted for each emotion separately, using emotion-specific datasets. This step yields optimized model weights \(\theta_{e}\), ensuring the model can synthesize speech with accurate emotional expressiveness. The difference between the fine-tuned model weights \(\theta_{e}\) and the pretrained model weights \(\theta_{pre}\) is defined as the emotion vector \(\phi_{e}\), which captures the influence of a specific emotion on speech characteristics and provides a controllable parameter for emotion expression. For flexible and precise control, we generate modified model parameters \(\theta_{e}^{\alpha}\) using:  
\[\theta_{e}^{\alpha}=\theta_{pre}+\alpha\phi_{e}. \tag{19}\]  
Unlike previous studies that control emotion intensity by training additional emotion intensity predictors, this work adopts an emotion arithmetic-based approach to achieve a more precise and efficient modulation of emotion strength. Taking the WET model [58] as an example, its emotion intensity modeling relies on training a regression network based on the relative attributes method, which learns the relative strength relations from a large number of speech samples and generates intensity labels as conditional inputs. Although effective, this strategy requires additional data annotation, model design, and optimization during training, significantly increasing system complexity and computational cost.