A method for style adaptation to spontaneous speech by using a semi-linear interpolation technique

This paper deals with a method for adapting a language model created fromwritten-text corpora to spontaneous speech by using a semi-linear interpolation technique. Sizes and topic coverages of spoken language corpora are usually far smaller those of written-text corpora. We propose an approach to adapt a base language model to the styles of spontaneous speech on the basis of the following assumptions. The words that are topic-independent, that is to say, common in spontaneous speech should be predicted mainly by a model created from spontaneous speech corpora (style model), while the base model is more reliable for predicting topic-related words, because they are di cult to predict from a model based on a small corpus. We classi ed all words into dis uencies and normal words. The normal words are classi ed into two more categories; common words and topic words according to mutual information. For each category, the quali ed models (base or style) with the optimal weights for linear interpolation are selected. In other words, a di erent linear combination of the models is used for each category of a predicted word. We conducted experiments by using a spoken-language corpus of Japanese for creating the style model. We achieved 159.1 in test-set perplexity compared with the baseline of 189.3 (simple linear interpolation) and the perplexity of the style speci c model, which was 230.7.