Artificial Intelligence Driven Gender Based Text-to-Speech System (TTS) Using Deep Learning Algorithms

Introduction: In this research article the researcher proposed a gender-based Text-to-Speech (TTS) system that uses specific voices to simulate either male or female speech, offering a variety of voices and accents to match the user's needs. These systems can be integrated into various applications to create more natural-sounding, and customized speech outputs. The main goal is to make synthetic speech more eminently plausible and natural sounding, closer to human speech in terms of suitability for the context and sound quality. Objectives: This research study aims to developed the limits of TTS technology by using AI; taking into account both the ethical issues and the technological constraints that must be overcome for general deployment. The ultimate objective is to create TTS systems that are widely accessible, morally acceptable, and able to be mistaken for human speech. Methods: The resrarcher implemented the deep neural networks (DNNs), such as recurrent and convolution neural networks (RNNs), along with more recent designs like Transformer and Generative Adversarial Networks (GANs). This research article investigates how AI technologies deep learning in particular can overcome these obstacles in a revolutionary way. Implementing and assessing cutting edge AI approaches to enhance TTS systems is the basis of this study. It is essential to this to employ deep neural networks (DNNs), such as recurrent and convolution neural networks (RNNs), along with more recent designs like Transformer and Generative Adversarial Networks (GANs). Results: The researcher achieved overall accuracy of 85% of the predictive model based on AI driven using CNN. Perfromance most likely can be improved by fine tuning, adding more data for training and possibly balancing target 50/50 instead of 60/40. It explores the shortcomings of conventional TTS systems, especially with regard to their ability to generate speech that is both natural and understandable. Conclusions: Finally the researcher concluded that the proposed model improving text to speech (TTS) systems naturalness and intelligibility using AI has been an extensive and fruitful endeavor, with several goals set out to do just that. Incorporating contextual and situational awareness, improving speech naturalness and intelligibility, prioritizing user centric assessment and optimization, and ensuring the ethical and responsible deployment of AI in TTS systems were all stated goals of this research. Extensive testing and analysis, however, revealed that prosody modeling is essential for improving speech realism. The researcher achieved the overall accuracy of 85%. Perfromance most likely can be improved by fine tuning, adding more data for training and possibly balancing target 50/50 instead of 60/40.