Collecting sentences from web resources for constructing spontaneous Chinese language model

In this paper, we present our work on collecting spontaneous texts from the Web for constructing a language model in a Chinese speech recognition system. The selection of spontaneous-like texts involves two steps: First, word-segmented web texts are selected using a perplexity-based approach in which the style-related words are strengthened by omitting infrequent topic words from similarity measurements. Second, the selected texts are then clustered based on non-noun part-of-speech (POS) words and optimal clusters are chosen by referring to a set of spontaneous seed sentences. Using the language model interpolated with the one trained by the selected sentences and a baseline model, speech recognition evaluations were conducted on an open domain spontaneous test set. We effectively reduced the character error rate (CER), with 1.64% absolute (or 6.5% relative) reduction by comparison with the baseline model. We also verified that the proposed method is superior to the conventional perplexity-based approach with about 1% absolute (or 4.0% relative) reduction in CER.