Definition
Neural TTS is a speech synthesis approach that uses deep neural networks to generate audio waveforms directly from text. Instead of assembling speech from pre-recorded phoneme fragments (concatenative synthesis) or using mathematical models (parametric vocoders), neural TTS learns the patterns of human speech from hundreds of hours of recorded audio.
The WaveNet breakthrough
Google DeepMind introduced WaveNet in September 2016. It generated audio sample by sample using deep convolutional neural networks, reducing the gap between synthetic and human speech by over 50%. Early versions required hours to generate one second of audio. By 2017, distillation techniques achieved a 1,000x speedup, enabling real-time generation.
Neural TTS in production
All major cloud TTS providers now offer neural voices as their primary product. Standard (non-neural) voices remain available at lower price points for applications where quality is less critical, such as IVR systems and notifications.
Frequently Asked Questions
What is neural TTS?
Neural TTS uses deep neural networks to generate speech by predicting audio waveforms sample by sample. Unlike concatenative synthesis, which splices pre-recorded fragments, neural TTS learns the statistical patterns that make speech sound human.
When did neural TTS replace traditional synthesis?
Google DeepMind's WaveNet in 2016 was the breakthrough. By 2022, neural TTS had replaced concatenative synthesis as the default across all major cloud providers.
Is neural TTS better than concatenative TTS?
In quality, yes. Neural TTS scores 4.5+ out of 5.0 on Mean Opinion Score tests, compared to 3.0-3.5 for concatenative systems. The trade-off is higher computational cost and latency.