Text-To-Speech (TTS), also known as Speech Synthesis, is a class of artificial intelligence technology that converts textual information into audible speech signals. Its goal is to enable computers to generate natural, coherent speech based on input text, allowing machines to output language content in a manner similar to human reading. TTS technology involves multiple fields such as natural language processing, speech signal processing, and machine learning, and is an important component of human-machine voice interaction systems.

The development of speech synthesis technology has evolved from rule-driven and concatenative synthesis to neural network-based generative synthesis. Early TTS systems primarily relied on manually designed language rules and recorded speech segments, generating speech through concatenation. Although these systems offered strong controllability, their naturalness was limited. With the advancement of deep learning, researchers began using neural networks to directly learn the mapping between text and speech. In 2017, Google researchers proposed the Tacotron model in the paper Tacotron: Towards End-to-End Speech Synthesis, which directly converts text sequences into acoustic features (spectrograms) and synthesizes speech waveforms using the Griffin-Lim algorithm, validating the feasibility of end-to-end speech synthesis. This approach was further developed in 2018 with Tacotron 2, which evolved into a complete neural network synthesis pipeline combined with a neural vocoder (WaveNet), becoming a significant research contribution in the field of neural network TTS.

Modern TTS systems typically include components such as text analysis, acoustic modeling, and speech waveform generation. The model first understands the characters, words, semantics, and prosodic information in the text, then predicts the corresponding acoustic representation (such as Mel-spectrogram), and finally generates continuous speech signals through a vocoder. In recent years, with the development of Transformers, Diffusion Models, and large-scale speech models, TTS systems have continuously improved in terms of speech naturalness, emotional expression, multilingual support, and voice style control.

TTS technology primarily addresses the problem that digital information cannot be directly conveyed through speech, enabling computers to convert textual content into interactive audio output. Currently, this technology has been widely applied in scenarios such as intelligent assistants, audiobooks, navigation announcements, customer service systems, accessibility assistance, and virtual humans, providing a more natural voice interface for human-machine interaction.