Through a system of high-quality feedback and a thorough Quality Assurance process by mother-tongue experts, imperfections are continuously corrected. In parallel, ReadSpeaker creates so-called neural voices, using techniques based on deep learning AI technology. This revolutionary method involves mapping linguistic properties to acoustic features using Deep Neural Networks (DNNs). An iterative learning process minimises objectively measurable differences between the predicted acoustic features and the observed acoustic features in the training set. One of the advantages of the new DNN TTS method is that the acoustic database can be much smaller than for a USS voice. Only a few hours of recorded speech are needed for a neural voice, compared to at least three times as many for a good quality USS voice. Also, the resulting speech is generally smoother and even more human-like. This makes developing new, smart ReadSpeaker TTS voices with even more lifelike, expressive speech and customizable intonation faster than ever.
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