End-to-end neural text-to-speech (TTS) systems convert raw text input directly into speech waveforms using a single neural network model, bypassing the need for intermediate linguistic or acoustic feature engineering. Unlike traditional TTS pipelines, which involve separate steps like text normalization, phoneme conversion, and waveform synthesis, end-to-end models learn to map text to audio in a unified way. This approach simplifies the system and often produces more natural-sounding speech.
The process typically involves two main stages. First, the text is encoded into a sequence of contextual embeddings using a neural network encoder, such as a transformer or a recurrent neural network. This step captures linguistic patterns, pronunciation rules, and contextual relationships between words. Next, a decoder generates a spectrogram (a time-frequency representation of audio) from these embeddings. Models like Tacotron 2 use attention mechanisms to align text tokens with corresponding audio segments, ensuring proper timing and prosody. Finally, a vocoder (e.g., WaveNet or WaveGlow) converts the spectrogram into a raw waveform. Modern systems like FastSpeech or VITS integrate these steps into a single network, using techniques like duration prediction or variational inference to improve efficiency and quality.
Key advantages include reduced complexity and improved naturalness. For example, systems like Google’s Tacotron 2 or Microsoft’s FastSpeech can handle rare words or complex sentence structures by learning directly from data rather than relying on handcrafted rules. Challenges include the need for large, high-quality text-audio datasets and computational resources for training. End-to-end TTS also struggles with rare edge cases, like highly expressive or emotional speech, where traditional rule-based systems might still have an advantage. However, ongoing advancements in model architectures and training techniques continue to address these limitations.