Unsupervised class-based language model adaptation for spontaneous speech recognition

This paper proposes an unsupervised, batch-type, class-based language model adaptation method for spontaneous speech recognition. The word classes are automatically determined by maximizing the average mutual information between the classes using a training set. A class-based language model is built based on recognition hypotheses obtained using a general word-based language model, and linearly interpolated with the general language model. All the input utterances are re-recognized using the adapted language model. The proposed method was applied to the recognition of spontaneous presentations and was found to be effective in improving the recognition accuracy for all the presentations. The best condition was found to be using 100 word classes, and in this condition 2.3% of the absolute value improvement in the word accuracy averaged over all the speakers was achieved.