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Text-to-Speech (TTS) refers to automatic generation of spoken audio from text using rule-based or neural synthesis methods. The concept covers quality, prosody, latency, privacy and integration requirements within product and platform architectures. It focuses on architectural choices, operational models and ethical considerations.
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Text-to-speech generates synthetic spoken language from written text.
Text-to-speech grew out of speech-synthesis research and early rule-based speech machines. Standardized web interfaces such as the W3C Speech Synthesis API made synthetic speech easier to integrate into applications and browsers; neural models subsequently improved naturalness and expressiveness.
A synthesis system analyzes text, determines pronunciation and prosody, and produces an audio signal. Voice, language, emphasis, latency and audio format are controlled through an interface. Quality depends on the model, text preparation and domain; privacy and consent matter for personal or sensitive content.
Written content is converted into audible speech by a machine.
Text analysis, pronunciation, prosody and signal generation jointly determine the audio output.
Applications select voice, interface, latency and safeguards for their use case.
Text-to-speech enables reading assistance and voice interfaces; architecture choices balance quality, latency, cost and privacy.
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