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Fitting class-based language models into weighted finite-state transducer framework

Citace: [] IRCING, P., PSUTKA, J. Fitting class-based language models into weighted finite-state transducer framework. In EUROSPEECH 2003 PROCEEDINGS. Geneva: ISCA, 2003. s. 1873-1876.
Druh: STAŤ VE SBORNÍKU
Jazyk publikace: eng
Anglický název: Fitting class-based language models into weighted finite-state transducer framework
Rok vydání: 2003
Místo konání: Geneva
Název zdroje: ISCA
Autoři: Pavel Ircing , Josef Psutka
Abstrakt EN: In our paper we propose a general way of incorporating class-based language models with many-to-many word-to-class mapping into the finite-state transducer (FST) framework. Since class-based models alone usually do not improve the recognition accuracy, we also present a method for an efficient language model combination. An example of a word-to-class mapping based on morphological tags is also given. Several word-based and tag-based language models are tested in the task of transcribing Czech broadcast news. Results show that class-based models help to achieve a moderate improvement in recognition accuracy.
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