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Underpinning all of this is **_machine learning_** and, more specifically, **_deep learning_**. **_Transformers_** are trained using vast amounts of data, learning from millions of hours of recorded speech. This process involves feeding the model both **_text_** and the corresponding **_audio_**, allowing it to learn the complex relationships between the two. The more data the model has to learn from, the better it becomes at generating high-quality speech. The training process often involves adjusting the parameters of the **_neural network_** to minimize the difference between the generated speech and the original **_audio_**. This is where **_deep learning_** comes in, using multiple layers of **_neural networks_** to extract increasingly complex features from the data. These layers allow the model to understand things like phonemes (the basic units of sound), prosody (the rhythm and intonation of speech), and the subtle variations that make a voice sound natural. It's a computationally intensive process, requiring powerful hardware and sophisticated algorithms, but the results are well worth the effort. The ongoing advancements in **_machine learning_** and **_deep learning_** are constantly pushing the boundaries of what's possible in **_voice synthesis_**. The models are becoming more accurate, more expressive, and better at capturing the unique characteristics of different voices. As the technology continues to evolve, we can expect to see even more impressive and realistic **_AI_**-generated voices in the future.
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Next comes the recording session. The voice actor works with the director, who provides direction and feedback. The director guides the actor in terms of delivery, pacing, and emotional tone. It's a collaborative process where the director and the voice actor work together to bring the character to life.
