Using dependency grammar features in whole sentence maximum entropy language model for speech recognition

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.