An empirical study of smoothing techniques for language modeling
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Speech and Language Processing (2nd Edition)
Speech and Language Processing (2nd Edition)
A morphosyntactic Brill Tagger for inflectional languages
IceTAL'10 Proceedings of the 7th international conference on Advances in natural language processing
Hand-Written and automatically extracted rules for polish tagger
TSD'06 Proceedings of the 9th international conference on Text, Speech and Dialogue
Importance of High-Order N-Gram Models in Morph-Based Speech Recognition
IEEE Transactions on Audio, Speech, and Language Processing
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This paper investigates the usefulness of a part of speech language model on the task of automatic speech recognition. The develped model uses part of speech tags as categories in a category-based language model. The constructed model is used to re-score the hypotheses generated by the HTK acoustic module. The probability of a given sequence of words is estimated using n-grams with Witten-Bell backoff. The experiments presented in this paper were carried out for Polish. The best obtained results show that the part-of-speech-only language model trained on a 1-million manually tagged corpus reduces the word error rate by more than 10 percentage points.