# ECE-TTS: A Zero-Shot Emotion Text-to-Speech Model with Simplified and Precise Control
## 1 Introduction
Recent research has explored speech disentanglement techniques to tackle this issue. For instance, the FACodec framework [2] decomposes speech into three components: content, prosody, and acoustic details. However, it has been observed that the prosody encoder encodes phonetic details, leading the synthesized speech to reflect the content of the emotion prompt audio instead of the intended text prompt [30]. The above two kinds of methods for controlling emotional style are typically limited by the finite types of emotions available in emotional datasets. This limitation often leads to synthesized audio with a lack of stylistic variety or stereotypical emotional expressions, making it difficult to achieve fine-grained emotional control [31; 32]. To address this issue, continuous emotional representations, such as the [PERSON] emotional model [33], are utilized to achieve more nuanced control over variations in emotional style. Based on [PERSON]'s Circumplex Model of Affect, emotional attributes can be represented in a continuous three-dimensional space defined by valence, arousal, and dominance. Valence reflects the degree of positivity or negativity of an emotion, arousal indicates the level of physiological or emotional activation, and dominance measures the extent to which an individual feelsin control or subordinate in a given emotional state. While this representation enables continuous modeling of affective states, further clarification of how fundamental emotions are distributed within this space enhances interpretability and control in emotional speech synthesis. While this continuous model supports nuanced emotional control, it is important to understand how canonical emotion types are positioned within the VAD space to further improve interpretability and synthesis precision. To this end, a structured emotional spectrum is typically formed using seven representative categories that are widely recognized in emotional speech datasets: neutral, happy, angry, sad, disgusted, excited, and fear. These categories not only represent distinct affective states, but also exhibit characteristic positions in the VAD space, as illustrated in Figure 1. Neutral is typically located near the center of the space, representing emotional balance, low activation, and the absence of strong valence or dominance. In contrast, happy occupies the region of high valence, high arousal, and strong dominance, conveying a positive and energetic affective state. Angry also exhibits high arousal and dominance but it is characterized by low valence, reflecting confrontation and tension. Sad lies in the low-valence, low-arousal, and low-dominance region, indicating a withdrawn and passive emotional state. Fear shares low valence and high arousal with anger but shows significantly lower dominance, denoting helplessness. Excited is close to happy in valence and arousal but tends to express greater outward intensity. Disgusted generally appears in the low-valence, moderate-arousal range, associated with rejection and aversion. This structured spectrum not only enhances understanding of the relationships among emotion categories but also provides a psychologically grounded reference framework for designing emotion control mechanisms. It supports interpretable emotion encoding, enables interpolation in the affective space, and offers a solid foundation for both data annotation and conditional generation in TTS. Moreover, the corresponding values of arousal, valence, and dominance can be automatically extracted using a pretrained emotion recognition model [34], eliminating the need for manual emotion annotation. Building on this foundation, a recent study has applied an emotion-adaptive coordinate transformation to convert VAD into an Emotion-Adaptive Spherical Vector (EASV) [35]. Experimental results indicate that using EASV as the emotion style representation enables more precise control over emotional styles. Emotion intensity, which reflects the degree of emotional expression along specific dimensions, plays a crucial role in emotional modeling.