Neural Text-to-Speech (Neural TTS) uses deep-learning models to convert written text into highly natural, expressive, human-like speech. Unlike older concatenative or parametric TTS, neural TTS generates audio end-to-end, capturing prosody, emphasis, and emotion, and can stream speech with very low latency for real-time use. Many systems also support SSML, multiple languages, and custom or cloned voices.
Neural TTS is the voice of modern AI: it powers customer support agents, AI receptionists, virtual assistants, accessibility readers, and multilingual voiceovers. The jump in naturalness is what makes AI phone calls sound human. Evaluate neural TTS on naturalness, expressiveness and SSML control, streaming latency, language coverage, and pricing.