Neural TTS (Neural Text-to-Speech)
Deep learning-based synthesis of human-sounding speech from text.
Neural TTS (Neural Text-to-Speech) refers to text-to-speech systems built on deep neural networks, as opposed to traditional concatenative or formant synthesis methods. Neural TTS produces significantly more natural, expressive, and human-sounding speech.
How neural TTS works
Neural TTS typically involves two stages:
- A sequence-to-sequence model (e.g., Tacotron 2, FastSpeech 2) that converts text into a mel-spectrogram — a visual representation of audio frequency over time
- A vocoder (e.g., WaveNet, HiFi-GAN) that converts the mel-spectrogram into a waveform (actual audio)
Modern end-to-end systems like VITS, StyleTTS 2, and VoiceBox handle both stages in a single model.
Quality metrics
- MOS (Mean Opinion Score): The standard metric for TTS quality, rated by human listeners on a 1–5 scale. State-of-the-art neural TTS achieves MOS scores of 4.2–4.6, approaching human speech quality (typically 4.5–4.8).
- Human-likeness benchmark: The percentage of listeners who cannot distinguish the synthetic voice from a real human in a blind A/B test.
TurboCall's neural TTS scores 99.7% on human-likeness benchmarks, meaning most listeners cannot tell they are hearing AI-generated speech.
Prosody and expressiveness
The key advantage of neural TTS over earlier methods is its ability to model prosody naturally. Neural voices express emphasis, question intonation, emotional tone, and conversational rhythm without requiring explicit markup.