Automatic clustering of part-of-speech for vocabulary divided PLSA language model

PLSA is one of the most powerful language models for adaptation to a target speech. The vocabulary divided PLSA language model (VD-PLSA) shows higher performance than the conventional PLSA model because it can be adapted to the target topic and the target speaking style individually. However, all of the vocabulary must be manually divided into three categories (topic, speaking style, and general category). In this paper, an automatic method for clustering parts-of-speech (POS) is proposed for VD-PLSA. Several corpora with different styles are prepared, and the distance between corpora in terms of POS is calculated. The "general tendency score" and "style tendency score" for each POS are calculated based on the distance between corpora. All of the POS are divided into three categories using two scores and appropriate thresholds. Experimental results showed the proposed method formed appropriate clusters, and VD-PLSA with acquired categories gave the highest performance of all other models. We applied the VD-PLSA into large vocabulary continuous speech recognition system. VD-PLSA improved the recognition accuracy for documents with lower out-of-vocabulary ratio, while other documents were not improved or slightly descended the accuracy.