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At the core of many voice cloning systems are *deep learning models*. These models are designed to learn from vast amounts of data, which in this case, would be audio recordings of the target voice. The model processes the audio data, identifying patterns and relationships that are characteristic of the voice. This might involve analyzing the frequency of sound waves, identifying the way the vocal cords vibrate, and modeling the way the speaker pronounces different phonemes (the basic units of sound in a language). The deep learning model then uses this information to generate new speech in the cloned voice. When you provide text, the AI system breaks the text down, figures out the phonemes, and then uses its model to recreate them in the target voice. The resulting audio sounds just like the original, because the AI has learned the distinct characteristics of the person’s speech.
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