In this paper we investigate the usefulness of morphosyntactic information as well as clustering in modeling Polish for automatic speech recognition. Polish is an inflectional language, thus we investigate the usefulness of an N-gram model based on morphosyntactic features. We present how individual types of features influence the model and which types of features are best suited for building a language model for automatic speech recognition. We compared the results of applying them with a class-based model that is automatically derived from the training corpus. We show that our approach towards clustering performs significantly better than frequently used SRI LM clustering method. However, this difference is apparent only for smaller corpora.
Accelerating Recurrent Neural Network Language Model Based Online Speech Recognition System