# ECE-TTS: A Zero-Shot Emotion Text-to-Speech Model with Simplified and Precise Control
## 1 Introduction
For example, happiness can range from pleasure to exuberance, while sadness can manifest as mild sorrow or deep grief [36]. However, most emotional speech datasets lack explicit emotion intensity labels, and compared to discrete emotion category annotations, labeling emotion intensity is a more subjective and complex process, posing additional challenges for emotion modeling. To address emotion intensity control, researchers have made numerous efforts to define and compute emotion intensity values effectively for model training. One of the most commonly used methods is Relative Attributes Ranking (RAR) [37], which has been widely applied in studies [38; 39; 40; 41; 42]. RAR constructs a ranking matrix through maximum margin optimization and solves it using a Support Vector Machine (SVM). The resulting rankings are then used for model training. Despite its popularity, RAR is a manually designed and independent stage, which may lead to suboptimal results and introduce biases during training. Beyond RAR, researchers have further explored emotion embedding space manipulations. For instance, ref. [43] proposed an algorithm that maximizes the distance between emotional embeddings and interpolates within the embedding space to control emotion intensity. Similarly, ref. [44] quantified the distances within the emotion embedding space to determine emotion intensity. However, the structure of the embedding space significantly impacts the performance of these models, necessitating careful constraint application to ensure stability and generalization [45]. Recently, Task Arithmetic has been introduced for emotion intensity control [46], extending its application from Natural Language Processing (NLP) and Computer Vision (CV) to emotional speech synthesis. Unlike manual ranking or embedding manipulations, this method leverages weight-space interpolation, enabling direct and efficient intensity modulation. By computing emotion vectors as the difference between pretrained and fine-tuned model weights, Task Arithmetic provides precise, scalable, and smooth emotion intensity control without additional ranking-based processes. In addition to model limitations and challenges in emotion control, the limited scale of existing emotional speech datasets and the high cost of manual emotion annotation also restrict the performance of emotional speech synthesis. While early studies, [PERSON] et al. [47] identified six basic emotions--happiness, anger, sadness, disgust, surprise, and fear--through cross-cultural facial expression studies. These emotions exhibit high consistency and distinguishability across different cultures. Most existing emotional speech datasets are built upon these fundamental emotions, ensuring high consistency and distinguishability across different cultures. Mainstream emotional datasets, such as ESD [48], IEMOCAP [49], and EXPRESSO [50], predominantly use discrete labels for emotion annotation, limiting expressiveness and diversity. Among them, IEMOCAP dataset includes continuous emotion annotations, offering a more refined representation of emotional variations. Specifically, the ESD dataset contains approximately 30 h of speech and covers five emotional categories; IEMOCAP spans 12 h and includes seven emotion categories, while EXPRESSO contains 47 h of speech and supports 26 different expressive styles, providing a broader range of emotional diversity. Despite progress in the construction of emotional speech datasets, most existing datasets remain relatively small in scale, with many containing less than 100 h of recorded speech. This limitation constrains the model's ability to learn diverse emotional patterns and compromises the quality of synthesized speech. Moreover, studies suggest that humans can perceive up to 34,000 distinct emotions [51],  
Figure 1: Three-dimensional valence–arousal–dominance (VAD) emotional space representation. far exceeding the number of emotions covered by current TTS systems.