2013 年 8 巻 2 号 p. 449-456
In this paper, we present our work on collecting training texts from the Web for constructing language models in colloquial and spontaneous Chinese automatic speech recognition systems. The selection involves two steps: first, web texts are selected using a perplexity-based approach in which the style-related words are strengthened by omitting infrequent topic words. Second, the selected texts are then clustered based on non-noun part-of-speech words and optimal clusters are chosen by referring to a set of spontaneous seed sentences. With the proposed method, we selected over 3.80M sentences. By qualitative analysis on the selected results, the colloquial and spontaneous-speech like texts are effectively selected. The effectiveness of the selection is also quantitatively verified by the speech recognition experiments. Using the language model interpolated with the one trained by these selected sentences and a baseline model, speech recognition evaluations were conducted on an open domain colloquial and spontaneous test set. We effectively reduced the character error rate 4.0% over the baseline model meanwhile the word coverage was also greatly increased. We also verified that the proposed method is superior to a conventional perplexity-based approach with a difference of 1.57% in character error rate.
