Text-to-speech (TTS) technology converts written text into natural-sounding audio using a pipeline of linguistic preprocessing, acoustic modeling, and vocoding. Modern neural TTS systems achieve near-human prosody and can be fine-tuned to bespoke voices with only minutes of reference audio.
Architecture Overview
Key Quality Metrics
Design Trade-offs
- Latency vs. Naturalness: Autoregressive models deliver superior prosody but decode slower than parallel FastSpeech-style models.
- Memory Footprint: HiFi-GAN vocoder ~2 M params vs WaveRNN 24 M; choose based on device constraints.
- Voice Consistency: Few-shot speaker adaption can drift over long passages; require speaker embeddings stabilization.
Current Trends (2025)
- Zero-shot voice cloning via large audio language models (e.g., Voicebox) with 2-second reference clips.
- Expressive TTS: Style tokens and emotion embeddings enable whispered, shouted, or singing outputs.
- On-device TTS: Apple Neural Engine and Qualcomm Sensing Hub run 30 ms latency offline voices.
- Multilingual Models: Seamless code-switching between 30+ languages in a single checkpoint.
Implementation Tips
- Normalize text (numbers, abbreviations) before G2P to avoid pronunciation errors.
- Cache vocoder kernels on GPU for low tail latencies.
- Post-process with dynamic range compression to equalize loudness across utterances.
References
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Valle et al., FastPitch: Parallel TTS with Pitch Prediction, 2021. ↩