# ECE-TTS: A Zero-Shot Emotion Text-to-Speech Model with Simplified and Precise Control
## 1 Introduction  
With the rapid advancement of Text-to-Speech (TTS) technology, modern speech synthesis models have achieved the capability to generate speech that closely resembles human voices and accurately convey semantic information [1, 2, 3, 4, 5, 6, 7]. However, human speech not only carries semantic content but also expresses rich emotional information through variations in intonation, making emotional speech synthesis a topic of significant interest in recent years. Emotion TTS has made significant progress in recent years, but still faces many challenges in emotion control, mainly due to the limitations of existing models, the inherent complexity of human emotions (including emotional style and intensity) [8], and the lack of adequate emotional datasets. Existing emotional speech synthesis models, such as Emo-VITS [9], E3-VITS [10], and YourTTS [11], which utilize the VITS model [12], as well as GenerSpeech [13] and EmoSphere-TTS [14], which are based on the FastSpeech 2 model [15], have been widelyadopted in the field of speech synthesis. However, these models generally produce audio with limited quality and restricted emotional expressiveness. In contrast, the recently proposed F5-TTS model, a non-autoregressive TTS framework based on Conditional Flow Matching and Diffusion Transformers (DiT) [16], offers a more streamlined and efficient approach. Recent research [17; 18; 19] indicates that removing phoneme-level duration predictors improves speech naturalness in zero-shot generation while eliminating the need for explicit phoneme alignment, thereby enhancing both naturalness and emotional diversity. Unlike conventional models such as VITS and FastSpeech 2, which rely on phoneme alignment and duration predictors, F5-TTS simplifies the synthesis pipeline by eliminating phoneme alignment, duration predictors, and text encoders, reducing architectural complexity. Additionally, it integrates ConvNeXt V2 [20] to enhance text representations and improve text-speech alignment during in-context learning. This design removes the constraints of traditional alignment mechanisms, leading to a more natural and expressive synthesis. Furthermore, F5-TTS employs the Sway Sampling strategy, which optimizes the sampling process in flow-based steps, further improving speech quality, naturalness, and speaker similarity in synthesized outputs. In addition to TTS models, effective emotion control is crucial for emotional speech synthesis. It can be broadly divided into emotional style control and emotional intensity control, each addressing different aspects of expressive speech generation. Emotional style control determines the type of emotion expressed in speech, shaping how an utterance conveys emotional intent. In this aspect, existing emotional TTS systems have primarily adopted two approaches. The first approach relies on supervised training using discrete labeled emotional datasets [21; 22; 23], where explicit labels are used to control emotional style. However, due to the complexity of human emotions, manually labeling emotional data is costly and time-consuming. As a result, existing datasets are typically short and lack diversity, limiting the effectiveness of this method and leading to synthesized speech with monotonous emotional styles. The second approach extracts emotional features from short reference audio that matches specific style characteristics through conditional formatting [24; 25; 26], a method also known as reference encoding [27], or style transfer [28]. This approach uses a reference encoder to extract style information from a reference audio sample and transfer it to the generated speech. While this method does not rely on explicit emotion labels and enables the synthesis of diverse emotional expressions, a major challenge lies in effectively disentangling emotional features from other attributes (e.g., speaker identity, age) present in the reference audio and transferring only the desired emotional style to the main synthesis network [29].