CASIA OpenIR  > 数字内容技术与服务研究中心  > 听觉模型与认知计算
Punctuation Prediction for Chinese Spoken Sentence Based on Model Combination
Chen, Xiao; Ke, Dengfeng; Xu, Bo
2013-11
Conference NameInternational Conference on Intelligent Systems and Knowledge Engineering
Source PublicationProceedings of the Eighth International Conference on Intelligent Systems and Knowledge Engineering
Conference Date20-23
Conference PlaceShenzhen
Abstract
Punctuation prediction is very important for automatic speech recognition. It greatly improves readability of transcripts and user experience, and facilitates following natural language processing tasks. In this study, we develop a model combination based approach for the recovery of punctuation for Chinese spoken sentence. Our approach models the relationships between punctuation and sentence by the different ways of sentence representation. And the relationships modeled are combined by multi-layer perception to predict punctuation (period, question mark and exclamation mark). Different from previous studies, our proposed approach is designed to use global lexical information, not only local information. Results indicate that, compared with the baseline, our proposed method results in an absolute improvement of 10.0% un-weighted accuracy and 4.9% weighted accuracy, respectively. Our approach finally achieves an un-weighted accuracy of 86.9% and a weighted accuracy of 92.4%.

KeywordPunctuation Prediction Model Combine Global Lexical Information
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11821
Collection数字内容技术与服务研究中心_听觉模型与认知计算
Corresponding AuthorChen, Xiao
AffiliationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Chen, Xiao,Ke, Dengfeng,Xu, Bo. Punctuation Prediction for Chinese Spoken Sentence Based on Model Combination[C],2013.
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