Improving Language Models for Mandarin Conversational Speech Recognition with Web Data
نویسندگان
چکیده
Lack of data is a problem in training language models for conversational speech recognition, particularly for languages other than English. Experiments in English have successfully used webbased text collection targeted for a conversational style to augment small sets of transcribed speech; here we look at extending these techniques to Mandarin. In addition, we investigate different techniques for topic adaptation. Experiments in recognizing Mandarin telephone conversations show that use of filtered web data leads to a 28% reduction in perplexity and 7% reduction in character error rate, with most of the gain due to the general filtered web data.
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