A Cache-Based Natural Language Model for Speech Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distributional clustering of words for text classification
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Topic-based mixture language modelling
Natural Language Engineering
A novel word clustering algorithm based on latent semantic analysis
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01
MIKI: a speech enabled intelligent kiosk
IVA'06 Proceedings of the 6th international conference on Intelligent Virtual Agents
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Verbal command and control systems are fairly common; almost all off-the-shelf speech recognition packages come with a way to perform various tasks through a voice command. Unfortunately, these systems require that the user utter the commands precisely in the format that it is expecting. These systems have a small number of grammar rules defined that are used to match against incoming utterances. Here, we present a method of using these same grammar rules to expand the capabilities of command and control engines to include semantically similar utterances. Latent Semantic Analysis (LSA) is used to connect specific grammar rules with the meanings underlying matching phrases resulting in utterances being matched to grammar rules even though the exact phrase did not match any specific rule. Experiments are described that determine the extent to which this method can be used and how accurate it is.