# ECE-TTS: A Zero-Shot Emotion Text-to-Speech Model with Simplified and Precise Control
## 3 Experiments
### Results and Analyze
#### 3.4.2 Results of Emotion Intensity Control Experiment  
Figure 5 illustrates the impact of scaling factor \(\alpha\) on emotion intensity control, showing systematic changes in Valence (V), Arousal (A), and Dominance (D) across different emotions (happiness, sadness, and anger). The results confirm that increasing \(\alpha\) effectively modulates emotional expression in a predictable manner. For happiness (Figure 5a), V, A, and D increase as \(\alpha\) grows from 0.6 to 1.4, reflecting a stronger perception of happiness. In contrast, for sadness (Figure 5b), all three dimensions decrease, aligning with the subdued nature of sad speech. For anger (Figure 5c), V declines, while A and D rise, indicating more intense and forceful speech. These trends align with expected emotional characteristics, demonstrating that ECE-TTS effectively scales emotion intensity through \(\alpha\) adjustment, ensuring smooth and controlled modulation of expressive speech synthesis.  
Following the objective evaluation, a subjective evaluation was conducted to further validate the model's controllability of emotion intensity. Using speech data with the emotion of happiness, participants rated four speech samples on a five-point scale (1 = not happy, 5 = extremely happy). The results, as illustrated in Figure 6, show a clear positive correlation between the emotion intensity scaling factor (\(\alpha\)) and perceived happiness ratings. Specifically, the pretrained model \(\theta_{pre}\) received the lowest happiness rating, with a median score close to 2.0. As the intensity factor \(\alpha\) increased, participants consistently perceived higher happiness levels. For example, at \(\alpha=0.6\), the median happiness rating increased to approximately 3.0, indicating a moderate perception of happiness. Similarly, at \(\alpha=1.0\), the perceived happiness continued to rise, and at \(\alpha=1.4\), it reached the highest level, with a median score close to 4.0. This demonstrates that higher \(\alpha\) values effectively amplify the perceived emotional intensity in synthesized speech. The results demonstrate a clear correlation between increasing \(\alpha\) and higher happiness ratings.  
Figure 5: The variations in Valence (V), Arousal (A), and Dominance (D) values compared to ground truth, as the emotional intensity scales for different emotions. (**a**) Happy: V, A, and D increase with intensity; (**b**) Sad: V, A, and D decrease with intensity; (**c**) Angry: V decreases, while A and D increase with intensity.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **WER** &
\begin{tabular}{c} **Aro-Val-Domin** \\ **SIM** \\ \end{tabular} & **Emo SIM** \\ \hline GT & 12.25 & 1 & 1 \\ Generspeech [13] & 16.34 & 0.627 & 0.563 \\ EmoSphere++ [35] & 15.08 & 0.656 & 0.578 \\ ECE-TTS & **13.91** & **0.679** & **0.594** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Objective evaluation of different models on WER, Aro-Val-Domin SIM, and Emo SIM. Bold values indicate the best scores among all methods for each evaluation metric.  
The results confirm that the emotion arithmetic vector-based intensity control mechanism effectively modulates variations in V, A, and D values, enabling precise adjustment of emotional intensity. By leveraging simple arithmetic operations on model weights, this approach achieves efficient and accurate intensity modulation, offering a more straightforward and effective solution compared to traditional emotion intensity control methods.