X. D. Huang, Alex Acero, Hsiao-Wuen Hon, Yun-Cheng Ju, J. Liu, S. Meredith, and M. Plumpe
Whistler Text-to-Speech engine was designed so that we can automatically construct the model parameters from training data . This paper will focus on recent improvements on prosody and acoustic modeling, which are all derived through the use of probabilistic learning methods. Whistler can produce synthetic speech that sounds very natural and resembles the acoustic and prosodic characteristics of the original speaker. The underlying technologies used in Whistler can significantly facilitate the process of creating generic TTS systems for a new language, a new voice, or a new speech style. Whisper TTS engine supports Microsoft Speech API  and requires less than 3 MB of working memory.
|Published in||Proc. of the Int. Conf. on Acoustics, Speech, and Signal Processing|
|Publisher||Institute of Electrical and Electronics Engineers, Inc.|
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