Abstract
Corpus-based stochastic language models have achieved significant success in speech recognition, but construction of a corpus pertaining to a specific application is a difficult task. This paper introduces a Case-Based Reasoning system to generate natural language corpora. In comparison to traditional natural language generation approaches, this system overcomes the inflexibility of template-based methods while avoiding the linguistic sophistication of rule-based packages. The evaluation of the system indicates our approach is effective in generating users’ specifications or queries as 98% of the generated sentences are grammatically correct. The study result also shows that the language model derived from the generated corpus can significantly outperform a general language model or a dictation grammar.
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Fan, Y., Kendall, E. (2005). A Case-Based Reasoning Approach for Speech Corpus Generation. In: Dale, R., Wong, KF., Su, J., Kwong, O.Y. (eds) Natural Language Processing – IJCNLP 2005. IJCNLP 2005. Lecture Notes in Computer Science(), vol 3651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11562214_86
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DOI: https://doi.org/10.1007/11562214_86
Publisher Name: Springer, Berlin, Heidelberg
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