Prosodic structure forming method based on prosodic phrase

Abstract

The invention provides a novel prosodic structure boundary division forming method based on prosodic phrases. The method combines machine learning with rules to greatly improve the accuracy of the prediction of Chinese text prosodic structure boundary. Prosodic phrase boundaries are firstly identified on the premise that input files goes through word segmentation and part of speech tagging, then prosodic word boundaries are formed by combining prosodic phrase boundary information, and finally a plurality rules are artificially added to carry out integral modification. In prosodic phrase and prosodic word boundary identification, characteristics are respectively designed and selected for establishing a characteristic template, and a prosodic phrase model and a prosodic word model are established by utilizing the maximum entropy algorithm for respectively identifying prosodic boundaries of two stages. In addition, aiming at the errors in identification of a maximum entropy model, an optimal rule is selected by utilizing an error-driven rule learning method to further improve the accuracy. Based on the method, the prosodic structure boundary division forming method based on prosodic phrases is provided, and the method can effectively improve the accuracy of prosodic structure prediction and the naturalness of speed synthesis.

Description

technical field [0001] The invention belongs to the field of computer man-machine communication, and designs a multi-layer prosodic structure generation method based on prosodic phrases. In the prediction of prosodic boundary structure, the present invention introduces a machine learning method, and performs hierarchical processing on prosodic phrases and prosodic words. On the premise of systematic analysis and induction, the feature templates of prosodic phrases and prosodic words are designed in a targeted manner, and machine learning methods such as maximum entropy algorithm are used to design a system that can effectively and accurately generate prosodic boundary information. in practical applications such as speech synthesis. Background technique [0002] With the development of information technology, language technology and computer technology, speech synthesis technology has attracted more and more people's attention. By inputting text, the computer can speak huma...

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Problems solved by technology

However, due to the independence assumption of its output, HMM cannot consider the characteristics of the context, which limits the selection of features.

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