Published Paper Details:

Replication of voice using Real Time Voice Cloning, which uses cutting-edge technology that enables the generation of a synthetic voice closely resembling the target speaker's voice. This process involves two main stages that is the training the model on a large dataset of the target voice and utilizing that model to generate real-time speech. Contrary to the traditional voice synthesis methods, which frequently require extensive computational resources and time for voice synthesis. The process typically utilizes deep learning models particularly those based on neural networks, to analyze the speaker's voice characteristics such as tone, pitch and speech patterns. The system extracts these features using an Encoder model which creates a unique speaker embedding representing the voice's identity. This embedding is combined with a Text-To-Speech synthesis model which can convert the textual input into speech that mimics the original speaker's voice. Real Time Voice Cloning aims to minimize the latency and computational cost, making it feasible for practical applications such as voice assistants and accessibility tools. This approach for real-time voice cloning leverages advanced techniques such as Generative Adversarial Networks, Wavenets, Autoencoders, and even Long Short-Term Memory Model. By optimizing both training and synthesis stages, our approach achieves a superior synthesis of the target speaker's voice with minimum delay. We estimate the performance of the model through various parameters such as intelligibility, naturalness, and speaker similarity. The results show that our methods offer a scalable solution for superior real-time voice Cloning with potential, diverse applications in both personal and professional domains. Applications range from personalized digital assistants to dubbing and entertainment, and ethical examination must be taken into account due to concerns about potential misuse in generating fake videos.