Unsupervised voice learning refers to the process of training a machine learning model to understand and interpret human speech without the need for labeled training data. In traditional supervised learning, the model is trained using labeled examples of speech, where each input is paired with the corresponding output. However, in unsupervised voice learning, the model learns to recognize patterns and structures within the speech data on its own, without explicit guidance from labeled examples. This approach allows the model to discover underlying features and representations within the speech data, leading to more flexible and adaptive speech recognition and synthesis systems.

Unsupervised voice learning often involves techniques such as autoencoders, variational autoencoders, and generative adversarial networks (GANs) to extract meaningful representations from the raw speech data. These methods enable the model to learn the inherent structure of the speech signals, identify relevant features, and ultimately improve its ability to understand and generate human speech.