CASIA OpenIR  > 数字内容技术与服务研究中心  > 听觉模型与认知计算
Experimental Comparison of Text Information based Punctuation Recovery Algorithms in Real Data
Chen, Xiao; Ke, Dengfeng; Xu, Bo
2013-10
Conference NameInternational Conference on Computer Science and Network Technology
Source PublicationProceedings of the Third International Conference on Computer Science and Network Technology
Conference Date12-13
Conference PlaceDalian
Abstract
Punctuation recovery is very important for automatic speech recognition (ASR). It greatly improves readability of transcripts and user experience, and facilitates following natural language processing tasks. The text information based method is one of the basic solutions of punctuation recovery. For analyzing the features of these algorithms, improving them and using them to develop practical system, this paper evaluates text information based punctuation recovery algorithms (HELM, CRF, RNNLM and GTI) in real data. Results show that GTI outperforms other algorithms for punctuation recovery, and ASR’s error is the main cause of performance degradation of all punctuation recovery algorithms. Finally, some suggestions are given.
KeywordPunctuation Recovery Real Data Text Information
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11822
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. Experimental Comparison of Text Information based Punctuation Recovery Algorithms in Real Data[C],2013.
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