How Text-to-Speech Works: Neural Models, Latency, and Deployment

Text-to-speech (TTS) technology has advanced from producing robotic, synthetic outputs to generating human-like speech that is nearly indistinguishable from real voices. This evolution has made TTS a critical interface between software and humans, particularly in applications where audio is preferable to reading, such as voice assistants and AI agents. Modern TTS systems are structured as two-stage pipelines comprising a frontend that converts text into a linguistic representation and a backend that generates audio from this representation. These systems are now integral to real-time and batch voice applications due to their ability to handle low latency and high-quality speech output. With the integration of large language models (LLMs), TTS infrastructure has evolved to support real-time conversation and voice interfaces. Developers must consider architectural choices, trade-offs in quality versus latency, and deployment options—cloud, self-hosted, or edge—when building TTS systems. As TTS continues to improve, challenges around privacy, consent, and potential misuse of voice cloning become increasingly important, necessitating careful consideration and implementation of safeguards.