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Using Dependency Grammar Features in Whole Sentence Maximum Entropy Language Model for Speech Recognition

TitleUsing Dependency Grammar Features in Whole Sentence Maximum Entropy Language Model for Speech Recognition
Publication TypeConference Paper
Year of Publication2010
AuthorsRuokolainen, T, Alumäe, T, Dobrinkat, M
Conference NameThe Fourth International Conference Baltic HLT 2010
PublisherIOS Press
Conference LocationAmsterdam, The Netherlands
ISBN Number978-1-60750-640-9
Abstract

In automatic speech recognition, the standard choice for a language model is the well-known n-gram model. The n-grams are used to predict the probability of a word given its n-1 preceding words. However, the n-gram model is not able to explicitly learn grammatical relations of the sentence. In the present work, in order to augment the n-gram model with grammatical features, we apply the Whole Sentence Maximum Entropy framework. The grammatical features are head-modifier relations between pairs of words, together with the labels of the relationships, obtained with the dependency grammar. We evaluate the model in a large vocabulary speech recognition task with Wall Street Journal speech corpus. The results show a substantial improvement in both test set perplexity and word error rate.

URLhttp://www.pinview.eu/files/Ruokolainen2010.pdf