A case-based reasoning approach for speech corpus generation

Authors:
Yandong Fan;Elizabeth Kendall
Affiliations:
School of Network Computing, Faculty of IT, Monash University, Australia;School of Network Computing, Faculty of IT, Monash University, Australia
Venue:
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Year:
2005

Citing 5
Cited 1

Speech recognition: theory and C++ implementation

Speech recognition: theory and C++ implementation
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition

Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
Trainable methods for surface natural language generation

NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Instance-based natural language generation

NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Stochastic language generation for spoken dialogue systems

ANLP/NAACL-ConvSyst '00 Proceedings of the 2000 ANLP/NAACL Workshop on Conversational systems - Volume 3

A case-based reasoning approach to automating the construction of multiple choice questions

ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development

Quantified Score

Hi-index	0.00

Visualization

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.