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Topic Classification of Speech Documents Based on the Word Fragment Network

Author: ChenJing
Tutor: ZhangLei
School: Harbin Engineering University
Course: Signal and Information Processing
Keywords: Speech Recognition Subject Classification Grid Confusion network
CLC: TN912.3
Type: Master's thesis
Year: 2010
Downloads: 20
Quote: 1
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Abstract


Voice files Subject Classification according to the properties of a document or the content of the speech , a large number of documents is mapped to multiple pre - defined theme , voice classification is an important research direction for speech recognition , has broad application prospects , to get people more and more and more attention . Along with the multimedia era , the Chinese language document classification technology becomes essential. In response to this situation , this article from building the Chinese audio and document classification system , the use of HTK platform the voice document recognition into one - best single candidate and lattice candidate two structures , combined with traditional text classification thinking , more than 8700 of the four categories the voice clips merger training test set of 748 voice files , voice the theme of the document is classified . The classification system to a single candidate one - best classification system for the baseline system , compared with the multi- candidate lattice classification system . In the baseline system to the traditional text classification system framework . In multi - candidate classification system , with the confusion network lattice structure optimization, and in confusion network structure based on word -chip Lexicon . The method uses the confusion network for the classification of the intermediate structure , highlighting the competitive relationship between the multi - candidate , to avoid the error caused by the traditional method of looking for the optimal path in the recognition result , in order to ensure the correct classification . And the introduction of the a posteriori probability can effectively reduce the influence of the identification error of classification results . The classifier design part of the current classic singular value decomposition method to construct a class center classifier . The experimental results show that , compared to the single candidate classification system , introducing confusion network the candidate classification system can provide more competitive candidate , in the feature items sparse vectors in the case of having a better classification performance .

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CLC: > Industrial Technology > Radio electronics, telecommunications technology > Communicate > Electro-acoustic technology and speech signal processing > Speech Signal Processing
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