To enhance the capability of text-to-speech and automatic speech recognition algorithms, Microsoft researchers developed a deep learning model that uses unsupervised learning, an approach not commonly used in this field, to improve the accuracy of the two speech tasks. By using the Transformer model, which is based on a sequence-to-sequence architecture, the team achieved a 99.84%
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