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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.
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 based on a dynamic 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.