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ABSTRACT

We propose new methods to take advantage of text in resource-rich languages to sharpen statistical language models in resource-deficient languages. We achieve this through an extension of the method of lexical triggers to the cross-language problem, and by developing a likelihood-based adaptation scheme for combining a trigger model with an N-gram model. We describe the application of such language models for automatic speech recognition. By exploiting a side-corpus of contemporaneous English news articles for adapting a static Chinese language model to transcribe Mandarin news stories, we demonstrate significant reductions in both perplexity and recognition errors. We also compare our cross-lingual adaptation scheme to monolingual language model adaptation, and to an alternate method for exploiting cross-lingual cues, via cross-lingual information retrieval and machine translation, proposed elsewhere.

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