End-to-end speech synthesis is a speech synthesis approach where the entire process, from a sequence of characters or phonemes to the generation of synthesized speech, is performed within a single neural network model without relying on explicit intermediate representations or separate modules for different stages of synthesis. In end-to-end speech synthesis, the [[vocoder]] is merged with the [[acoustic model]], allowing the system to be trained end-to-end without requiring an [[intermediate spectrogram]]. The end-to-end approach offers several advantages: 1. Simplified architecture: By combining multiple synthesis stages into a single model, the complexity and design of the overall system are simplified, making it easier to develop and maintain. 2. Improved naturalness: End-to-end models can potentially capture complex relationships between input text and acoustic features, leading to more natural and coherent synthesized speech. 3. Reduced data requirements: End-to-end models can often be trained with less labeled data compared to traditional systems, as they can learn to directly generate speech without relying on explicit linguistic or acoustic feature alignments. 4. Faster development and deployment: The streamlined nature of end-to-end models allows for faster development and deployment of speech synthesis systems, making it easier to iterate and adapt to specific requirements or application scenarios. However, end-to-end speech synthesis also has some limitations: 1. Lack of interpretability: The black-box nature of end-to-end models can make it difficult to understand and control specific aspects of the synthesis process, such as prosody or individual phonetic details. 2. Limited control over intermediate stages: Since end-to-end models directly generate speech without explicit intermediate representations, controlling specific linguistic or acoustic aspects of the synthesized speech may be more challenging compared to modular systems. Examples of end-to-end systems for speech synthesis are [[GlowTTS architecture|GlowTTS]], [[VITS architecture|VITS]], and [[YourTTS architecture|YourTTS]]