Text-to-speech (TTS) is an AI technology that converts written text into spoken audio, synthesizing a human-sounding voice that reads the provided text aloud. Modern AI-powered TTS systems have advanced dramatically from the robotic, mechanical voices of earlier generations, producing natural-sounding speech with appropriate prosody, rhythm, and emotional inflection that can be difficult to distinguish from recorded human speech.

Contemporary TTS systems use neural network architectures trained on large datasets of human voice recordings to learn the acoustic characteristics, timing, and emotional qualities of natural speech. They can produce multiple voice styles, accents, and languages, adjust speaking pace and emphasis, and in some systems clone specific voices from short audio samples to produce speech that sounds like a particular person. Leading TTS platforms offer a range of voices designed for different use cases - authoritative narration voices for documentary content, friendly conversational voices for social media, and character voices for entertainment applications. The ability to generate high-quality speech from text has made professional-sounding voice-over production accessible without recording sessions or voice talent fees.

In AI video production workflows, text-to-speech is commonly used to generate narration, voice-over, and dialogue audio that accompanies generated visual content. Pairing AI-generated video with synthesized speech enables fully AI-produced video content - from explainer videos and social media clips to longer narrative pieces - without requiring any recorded audio, significantly reducing the resource requirements for producing polished audiovisual content.