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Machine Learning and Data Mining in Pattern Recognition

Volume 7988 of the series Lecture Notes in Computer Science pp 492-504

Using Part of Speech N-Grams for Improving Automatic Speech Recognition of Polish

  • Aleksander PohlAffiliated withDepartment of Electronics, AGH University of Science and TechnologyDepartment of Computational Linguistics, Jagiellonian University
  • , Bartosz ZiółkoAffiliated withDepartment of Electronics, AGH University of Science and Technology

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Abstract

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.