Comparison of Approaches for Predicting Break Indices in Mandarin Speech Synthesis
Abstract
This study adopts a large-scale corpus with five-tier break indices annotated according to C-TOBI. Based on it, several approaches, N-gram, Markov model and decision tree learning are applied to predict break indices automatically for unrestricted mandarin text. These approaches differ mutually not only in model, but also on features and even part-of-speech tag size. A deep comparison and analysis among these approaches was made in the research.
DOI: https://doi.org/10.3844/jcssp.2006.660.664
Copyright: © 2006 Shao Yan-qiu, Zhao Yong-zhen, Han Ji-qing and Liu Ting. This is an open access article distributed under the terms of the
Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Keywords
- Markov models
- speech synthesis
- break indices
- n-gram
- decision tree