Handling OOV Words in Mandarin Spoken Term Detection with an Hierarchical <i>n</i>-Gram Language Model

WANG Xuyang; ZHANG Pengyuan; NA Xingyu; PAN Jielin; YAN Yonghong

doi:10.1049/cje.2017.07.004

Volume 26 Issue 6

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WANG Xuyang, ZHANG Pengyuan, NA Xingyu, PAN Jielin, YAN Yonghong. Handling OOV Words in Mandarin Spoken Term Detection with an Hierarchical n-Gram Language Model[J]. Chinese Journal of Electronics, 2017, 26(6): 1239-1244. doi: 10.1049/cje.2017.07.004

Citation:

WANG Xuyang, ZHANG Pengyuan, NA Xingyu, PAN Jielin, YAN Yonghong. Handling OOV Words in Mandarin Spoken Term Detection with an Hierarchical n-Gram Language Model[J]. Chinese Journal of Electronics, 2017, 26(6): 1239-1244. doi: 10.1049/cje.2017.07.004

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Handling OOV Words in Mandarin Spoken Term Detection with an Hierarchical n-Gram Language Model

doi: 10.1049/cje.2017.07.004

1.
The Key Laboratory of Speech Acoustics and Content Understanding, Chinese Academy of Sciences, Beijing 100190, China;
2.
Xinjiang Laboratory of Minority Speech and Language Information Processing, Chinese Academy of Sciences, Urumqi 830011, China

Funds: This work is supported by the National Natural Science Foundation of China (No.11461141004, No.61271426, No.11504406, No.11590770, No.11590771, No.11590772, No.11590773, No.11590774), the Strategic Priority Research Program of the Chinese Academy of Sciences (No.XDA06030100, No.XDA06030500, No.XDA06040603), National 863 Program (No.2015AA016306), National 973 Program (No.2013CB329302) and the Key Science and Technology Project of the Xinjiang Uygur Autonomous Region (No.201230118-3).

More Information

Corresponding author: ZHANG Pengyuan (corresponding author) received the Ph.D. degree in information and signal processing from Institute of Acoustics, Chinese Academy of Sciences, in 2007. From 2013 to 2014, he was a research scholar of University of Sheffield. (Email:zhangpengyuan@hccl.ioa.ac.cn)
Received Date: 2015-08-28
Rev Recd Date: 2015-12-24
Publish Date: 2017-11-10

Abstract

Abstract

In this paper, an hierarchical n-gram Language model (LM) combining words and characters is explored to improve the detection of Out-of-vocabulary (OOV) words in Mandarin Spoken term detection (STD). The hierarchical LM is based on a word-level LM, with a character-level LM estimating probabilities of OOV words in a class-based way. The region containing OOV words in the sentence to be decoded is detected with the help of the word-level LM and the probabilities of OOV words are derived from the character-level LM. The implementation of the proposed approach is based on a dynamic decoder. The proposed approach is evaluated in terms of Actual term weighted value (ATWV) on two Mandarin data sets. Experiment results show that more than 10% relative improvement for OOV word detection is achieved on both sets. In addition, the detection of In-vocabulary (IV) words is barely influenced as well.
- Spoken term detection (STD),
- Language model (LM),
- Out-of-vocabulary (OOV) words

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References(21)

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