Method and device for discriminating pronunciation of polyphonic characters based on deep neural network

Abstract

The present application proposes a method and device for discriminating the pronunciation of polyphonic characters based on a deep neural network, wherein the method for discriminating the pronunciation of polyphonic characters based on a deep neural network includes: performing quantization coding on each Chinese character in the text to be recognized, and generating the The feature vector of the Chinese character; according to the feature vector of the Chinese character, combined with the context information of the two directions of the Chinese character, the input feature of the Chinese character is generated; the input feature of the Chinese character is respectively input into the DNN model corresponding to the initial consonant, the final syllable and the tone, and respectively obtains the first First probability, second probability and third probability; calculate the probability of various combinations of initials, finals and tones according to the first probability, second probability and third probability, and use the combination with the highest probability as the pronunciation of the Chinese character. The application can improve the accuracy of pronunciation discrimination, effectively alleviate the problem of unbalanced training samples caused by high-frequency sounds of polyphonic characters, and can solve the problem of pronunciation discrimination of multiple polyphonic characters, which is beneficial to the integration of speech synthesis systems.

Description

technical field [0001] The present application relates to the technical field of speech synthesis, and in particular to a method and device for discriminating the pronunciation of polyphonic characters based on a deep neural network. Background technique [0002] Word-to-sound conversion is an essential part of the Chinese speech synthesis system, and its accuracy directly affects the intelligibility of speech synthesis. Most Chinese characters have definite pronunciations, and the correct pronunciations can be obtained by searching a dictionary. However, there are many characters in Chinese that have two or more pronunciations. The key points and difficulties of word-sound conversion are the discrimination and disambiguation of these polyphonic characters. It is generally believed that the pronunciation of polyphonic characters is usually closely related to specific contextual information, semantics and language habits. How to automatically distinguish and analyze the pr...

AI Technical Summary

Problems solved by technology

[0006] However, the polyphone disambiguation method adopted in the existing related art relies on the contextual word segmentation information of limited distance, and the accuracy rate of polyphonic pronunciation discrimination is not high in texts modified by long attributives or long adverbials; That is, the pronunciation that often appears in the text), the model tends to predict a common pronunciation, and the prediction of the uncommon pronunciation is inaccurate; the rules or models obtained by training are only effective for a certain polyphonic character, and multiple polyphonic characters need to be trained more A model or rule, not suitable for training and pronunciation prediction of texts containing multiple polyphonic characters

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