What is end-to-end neural TTS, and how does it differ from traditional methods?

End-to-end neural text-to-speech (TTS) is a system that converts raw text directly into speech waveforms using a single neural network model, bypassing the multi-stage pipelines of traditional TTS approaches. Unlike traditional methods, which rely on handcrafted linguistic features, intermediate representations (like phonemes or prosody markers), and separate components for synthesis, end-to-end neural TTS trains a unified model to handle the entire process. This approach simplifies the workflow by learning mappings from text to audio implicitly through data, reducing manual engineering and enabling more natural-sounding output.

Traditional TTS systems typically involve three stages: text analysis (normalizing text, predicting phonemes), acoustic modeling (generating spectral features like Mel spectrograms), and waveform synthesis (using vocoders like WaveGAN or Griffin-Lim). For example, older systems like concatenative TTS stored pre-recorded speech units and pieced them together, often resulting in robotic or inconsistent audio. Statistical parametric TTS (e.g., HMM-based models) improved flexibility by generating features algorithmically but still required explicit control of pitch, duration, and other parameters. These stages often introduced errors that compounded across components, such as mispronunciations or unnatural prosody, and required domain expertise to tune each module.

In contrast, end-to-end neural TTS models like Tacotron 2 or VITS combine text analysis, acoustic modeling, and waveform synthesis into a single neural network. For instance, Tacotron 2 uses a sequence-to-sequence model to predict Mel spectrograms from text, followed by a WaveNet-like vocoder to generate waveforms. Modern variants like FastSpeech 2 further optimize speed and stability by parallelizing spectrogram generation. These models learn directly from paired text-audio data, capturing nuances like intonation and emphasis without explicit rules. While traditional methods require labeled linguistic data (e.g., phoneme alignments), end-to-end systems often work with raw text and audio, reducing preprocessing. However, they demand large datasets and computational resources for training. The key trade-off is simplicity and quality versus upfront training costs—end-to-end systems minimize manual design but rely heavily on data quantity and quality to generalize well.