Abstract
In this paper, the comparison of 2,3,4-gram language models with various lexicon sizes is presented. The text data forming the training corpus has been collected from recent Internet news sites; total size of the corpus is about 350 million words (2.4 GB data). The language models were built using the recognition lexicons of 110K, 150K, 219K, and 303K words. For evaluation of these models such characteristics as perplexity, OOV words rate and n-gram hit rate were computed. Experimental results on continuous Russian speech recognition are also given in the paper.
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Kipyatkova, I., Karpov, A. (2013). Lexicon Size and Language Model Order Optimization for Russian LVCSR. In: Železný, M., Habernal, I., Ronzhin, A. (eds) Speech and Computer. SPECOM 2013. Lecture Notes in Computer Science(), vol 8113. Springer, Cham. https://doi.org/10.1007/978-3-319-01931-4_29
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DOI: https://doi.org/10.1007/978-3-319-01931-4_29
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