Published Paper Details:

Text-to-Speech (TTS) synthesis has undergone significant progress, advanc- ing from initial rule-based and concatenative approaches to powerful deep learning-based architectures. This overview is a thorough coverage of the history, present methods, and potential future directions of TTS systems. We survey cutting-edge models like WaveNet, Tacotron, FastSpeech, and Flowtron, underscoring their advances in making speech more natural, intel- ligible, and efficient to synthesize. The combination of transformer models and self-supervised learning has also further improved TTS performance, particularly in multilingual and low-resource conditions. End-to-end, neu- ral vocoding, and adversarial training have greatly enhanced the quality of speech, and as a result, real-time solutions are applied everywhere from acces- sibility platforms to virtual assistants, audiobooks, and entertainment sites. Yet there is still some problem in prosody modeling, emotion expressiveness, and coping with various linguistic environments. In this paper, those limita- tions and their necessity in considering hybrid models, multimodal TTS sys- tems, and reinforcement training are explored. We also examine the ethical aspects of synthetic speech, including misuse threats and biases, highlight- ing the demand for secure, equitable, and responsible deployment. Overall, this review summarizes the key breakthroughs and upcoming trends in TTS synthesis while envisioning future research directions to develop resilient, adaptive, and human-like speech systems for diverse global applications.