What is Text-to-Speech (TTS)

How It Works

Modern TTS systems use a two-stage pipeline: a text-to-spectrogram model that converts text into a mel spectrogram capturing speech patterns, and a vocoder that converts the spectrogram into an audio waveform. The first stage handles linguistic aspects (pronunciation, prosody, pacing), while the vocoder generates high-fidelity audio. End-to-end models combine both stages.

Technical Details

Leading architectures include Tacotron 2 (autoregressive), FastSpeech 2 (non-autoregressive, faster), VITS (end-to-end), and XTTS (multi-speaker, multilingual). Neural vocoders like HiFi-GAN generate 24kHz audio at real-time or faster speeds. Zero-shot voice cloning models can mimic a speaker's voice from a short reference clip. Quality is evaluated using Mean Opinion Score (MOS) from human listeners.

Best Practices

• Choose non-autoregressive models (FastSpeech 2) for low-latency applications
• Use SSML (Speech Synthesis Markup Language) to control pronunciation and emphasis
• Validate output quality across different text types (questions, numbers, abbreviations)
• Implement streaming TTS for real-time applications to minimize time-to-first-audio

Common Pitfalls

• Not handling text normalization for numbers, dates, URLs, and abbreviations before synthesis
• Using a single-speaker model when diverse voices are needed for production
• Ignoring prosody and emphasis, resulting in monotone and unnatural speech
• Not considering ethical implications of voice cloning without consent

Advanced Tips

• Use TTS to generate audio versions of text content for multimodal content augmentation
• Implement voice cloning with speaker embeddings for personalized voice interfaces
• Combine TTS with emotion control for expressive speech generation
• Apply TTS for data augmentation in ASR training by generating synthetic speech