A Case-Based Reasoning Approach for Speech Corpus Generation

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.

• Type: Conference
• Year: 2005
• Where: IJCNLP
• Authors: Yandong Fan, Elizabeth A. Kendall
• Keywords: Corpus-based Stochastic Language, IJCNLP 2005, Language Model, Natural Language, Natural Language Processing