Advancing Text-To-Speech Synthesis Using Efficient Deep Learning Techniques For Enhanced Communication Recognition

Abstract

Advances in deep learning have significantly improved text-to-speech (TTS) synthesis, revolutionizing voice recognition technologies and applications. This study investigates the impact of state-of-the-art deep learning approaches on TTS systems, focusing on enhancing speech naturalness, intelligibility, and usability. Specifically, this work explores advanced neural network architectures, including transformer models and generative adversarial networks (GANs), that have set new benchmarks in generating high-quality synthetic speech. Through a comparative evaluation of these methods in diverse communication scenarios, our work highlights the practical potential of modern TTS synthesis techniques. It provides insights into their future trajectory toward enabling more inclusive and effective conversational systems.