Text-to-Speech (TTS) synthesis is a branch of speech processing and deep learning that converts written text into natural-sounding human speech. Modern neural TTS has transformed from rule-based and concatenative systems to end-to-end generative models that can produce highly realistic, expressive, and emotionally rich speech. At its core, TTS involves two main stages: converting text into intermediate acoustic representations (like mel-spectrograms), and then generating raw audio waveforms through a vocoder. The field has seen explosive growth with architectures like Tacotron, FastSpeech, and VITS enabling both high quality and fast inference. A critical insight: the choice between autoregressive (sequential, higher quality) and non-autoregressive (parallel, faster) models fundamentally shapes the trade-off between naturalness and speed, while the vocoder determines final audio fidelity and computational cost.