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<p>In this research article the researcher emphasized the significant progress has been made in a number of technical fields, including text to speech (TTS) systems, advent of artificial intelligence (AI). 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. From early concatenated approaches to the introduction of neural network based models, the paper firstly offers a thorough overview of the historical evolution of TTS systems, emphasizing significant turning points. 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). The paper then 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). The researcher achieved and 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. It explores the shortcomings of conventional TTS systems, especially with regard to their ability to generate speech that is both natural and understandable. The research explores the process of training these models on extensive datasets to better understand prosody, intonation, and rhythm in human speech, which will improve the outputs naturalness. This work has led to the creation of a unique hybrid model that maximizes naturalness and intelligibility by combining the best features of much neural architecture. This research study based on “Artificial Intelligence Driven Gender Based Text-to-Speech Systems(TTS) Using Deep Learning Algorithms” is original and not copied from any other sources. The researcher assure that this research article is not submitted to any other publication.</p>

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Artificial intelligence driven gender based text-to-speech systems(TTS) using deep learning algorithms

  • 期刊论文

  • 发表日期：

    2025年10月8日

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  • A. Siva Kumar Reddy,
  • Bechoo Lal,
  • K. Arun Bhaskar,
  • M. Bhaskar,
  • Ashish Ashish,
  • Dineshwari Bisen

摘要

In this research article the researcher emphasized the significant progress has been made in a number of technical fields, including text to speech (TTS) systems, advent of artificial intelligence (AI). 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. From early concatenated approaches to the introduction of neural network based models, the paper firstly offers a thorough overview of the historical evolution of TTS systems, emphasizing significant turning points. 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). The paper then 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). The researcher achieved and 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. It explores the shortcomings of conventional TTS systems, especially with regard to their ability to generate speech that is both natural and understandable. The research explores the process of training these models on extensive datasets to better understand prosody, intonation, and rhythm in human speech, which will improve the outputs naturalness. This work has led to the creation of a unique hybrid model that maximizes naturalness and intelligibility by combining the best features of much neural architecture. This research study based on “Artificial Intelligence Driven Gender Based Text-to-Speech Systems(TTS) Using Deep Learning Algorithms” is original and not copied from any other sources. The researcher assure that this research article is not submitted to any other publication.