Analysing recognition errors in unlimited-vocabulary speech recognition
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
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ACLDemos '10 Proceedings of the ACL 2010 System Demonstrations
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ACM Transactions on Speech and Language Processing (TSLP)
Unsupervised vocabulary adaptation for morph-based language models
WLM '12 Proceedings of the NAACL-HLT 2012 Workshop: Will We Ever Really Replace the N-gram Model? On the Future of Language Modeling for HLT
Using part of speech n-grams for improving automatic speech recognition of polish
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Speech recognition systems trained for morphologically rich languages face the problem of vocabulary growth caused by prefixes, suffixes, inflections, and compound words. Solutions proposed in the literature include increasing the size of the vocabulary and segmenting words into morphs. However, in many cases, the methods have only been experimented with low-order n-gram models or compared to word-based models that do not have very large vocabularies. In this paper, we study the importance of using high-order variable-length n-gram models when the language models are trained over morphs instead of whole words. Language models trained on a very large vocabulary are compared with models based on different morph segmentations. Speech recognition experiments are carried out on two highly inflecting and agglutinative languages, Finnish and Estonian. The results suggest that high-order models can be essential in morph-based speech recognition, even when lattices are generated for two-pass recognition. The analysis of recognition errors reveal that the high-order morph language models improve especially the recognition of previously unseen words.