Abstract
In text-to-speech (TTS) system, polyphone disambiguation is the significant and difficult point in the process of grapheme-to-phoneme(G2P) conversion. In the existing methods, polyphone disambiguation mainly uses statistics or context information. When the data to be predicted is short texts or spoken language, it is difficult for the previous models to distinguish the correct pronunciation due to insufficient information. Therefore, this paper intends to report an improved design for polyphone disambiguation. Semantic extension has been used through the method of feature fusion, we creatively encode the context feature, word meaning feature, part of speech feature and pronunciation feature of polyphone, and construct three kinds of polyphone disambiguation models using bidirectional LSTM neural network. It is shown in the experiment that the accuracy of the polyphone disambiguation model based on word meaning can be improved by about 7% compared with the traditional methods.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
