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Improving language modeling by using distance and co-occurrence information of word-pairs and its application to LVCSR.

, , , and . ICASSP, page 4883-4887. IEEE, (2014)

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Improving language modeling by using distance and co-occurrence information of word-pairs and its application to LVCSR., , , and . ICASSP, page 4883-4887. IEEE, (2014)The development and analysis of a Malay broadcasr news corpus., , , , , , , and . O-COCOSDA/CASLRE, page 1-5. IEEE, (2013)On the study of very low-resource language keyword search., , , , , , and . APSIPA, page 358-364. IEEE, (2015)TDTO language modeling with feedforward neural networks., , , and . INTERSPEECH, page 1458-1462. ISCA, (2015)Multi-space random mapping for speaker identification., , , and . IEICE Electron. Express, 2 (7): 226-231 (2005)An Empirical Evaluation of Stop Word Removal in Statistical Machine Translation., , and . ESIRMT/HyTra@EACL, page 30-37. Association for Computational Linguistics, (2012)TransfoRNN: Capturing the Sequential Information in Self-Attention Representations for Language Modeling., , , and . CoRR, (2021)Modeling of term-distance and term-occurrence information for improving n-gram language model performance., , , and . ACL (2), page 233-237. The Association for Computer Linguistics, (2013)Exploiting long context using joint distance and occurrence information for language modeling. Nanyang Technological University, Singapore, (2018)Improving Efficiency of Sentence Boundary Detection by Feature Selection., , , , and . ACIIDS (2), volume 9622 of Lecture Notes in Computer Science, page 594-603. Springer, (2016)