End-to-end neural text-to-speech (TTS) is a method that uses deep learning models to directly convert raw text into speech waveforms without relying on intermediate, manually designed processing steps. Unlike traditional TTS systems, which break the task into separate stages (text analysis, acoustic modeling, and waveform synthesis), end-to-end models train a single neural network to handle the entire pipeline. For example, architectures like Tacotron 2 or FastSpeech use sequence-to-sequence models with attention mechanisms to map text characters or phonemes directly to spectrograms, which are then converted to audio using vocoders like WaveNet or Griffin-Lim. This approach minimizes human-engineered components, allowing the model to learn latent patterns between text and speech data directly.
Traditional TTS systems, such as concatenative or parametric methods, rely on explicit, modular processing. Concatenative synthesis stitches together pre-recorded speech units (like diphones or triphones) from a database, requiring extensive linguistic rules to select and join units smoothly. Parametric approaches, like Hidden Markov Models (HMMs) or early deep neural networks (DNNs), generate acoustic features (e.g., pitch, duration) and use vocoders to synthesize audio. These methods demand meticulous feature engineering—such as grapheme-to-phoneme conversion, prosody prediction, and stress modeling—to handle nuances like pronunciation, intonation, and rhythm. For instance, systems like Festival or MaryTTS use handcrafted rules and dictionaries to normalize text, resolve homographs (“read” vs. “read”), and align linguistic features with acoustic outputs. This modularity allows fine-grained control but introduces complexity and brittleness when adapting to new languages or voices.
The key differences lie in flexibility, data efficiency, and output quality. End-to-end neural TTS reduces development complexity by learning mappings directly from data, often producing more natural-sounding speech with fewer artifacts. However, it requires large datasets and computational resources for training, and it can struggle with rare words or edge cases without explicit linguistic constraints. Traditional methods, while less natural-sounding, offer transparency and control—for example, tweaking pronunciation rules or adjusting prosody parameters. End-to-end models excel in scalability and generalization but trade off interpretability, making them better suited for applications prioritizing naturalness over fine-grained customization.