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Résumé : Voice conversion technology has made great leaps with the incorporation of machine learning, which allows for effortless conversion of speech without losing the speaker's distinctive features. With the use of deep learning, it is now feasible to change the tone, style, and speech habits of a target speaker while retaining the linguistic content of a source speaker. VoiceCraft presents a novel voice-to-voice conversion strategy that leverages advanced neural architectures to deliver high-fidelity speech transformation. In contrast to conventional statistical approaches, which tend to falter when preserving naturalness, this system uses convolutional neural networks (CNNs) for feature extraction, transformers for phoneme mapping, and generative adversarial networks (GANs) for natural voice synthesis. The system starts with the extraction of fundamental voice features from both the source and target speakers. A pretrained speaker embedding model like Wav2Vec or ECAPA-TDNN captures distinctive vocal features, so the output voice is a true representation of the target speaker's tone and style. A phoneme alignment module then creates accurate mappings between the source and target voices, maintaining clarity and consistency in the translated speech. Deep neural networks also fine-tune speech features, such as prosody, pitch, and intonation, to produce a natural and expressive output. To make the overall speech sound more quality, a neural vocoder such as WaveNet or HiFi-GAN synthesizes high-resolution waveforms, so the voice produced sounds natural and smooth. The broad variety of applications, such as speech synthesis, voice dubbing, assistive communication devices, and AI-powered virtual assistants. By providing individualized and adaptive voice conversion, the system adds value to user experiences in entertainment, accessibility, and interactive AI systems. Future development will be directed towards real-time processing, cross-lingual voice conversion, and enhanced robustness in variable acoustic environments. Increasing the dataset and using self-supervised learning methods can further increase adaptability, with the result being more accurate and efficient voice conversion across languages and dialects.