ISCA Archive ICSLP 2000
ISCA Archive ICSLP 2000

Toward unconstrained command and control: data-driven semantic inference

Jerome R. Bellegarda, Kim E. A. Silverman

Command and control tasks are typically approached using a context-free grammar as language model. While ensuring a good system performance, this imposes a rigid framework on users, by implicitly forcing them to conform to a pre-defined interaction structure. This paper introduces the concept of data-driven semantic inference, which in principle allows for any word constructs in command/query formulation. Each unconstrained word string is automatically mapped onto the intended action through a semantic classification against the set of supported actions. The underlying (latent semantic analysis) framework relies on co-occurrences between words and commands, as observed in a training corpus. A suitable extension can also handle commands that are ambiguous at the word level. Experiments conducted on a desktop command and control task involving 113 di erent actions show a classification error rate of 1.7%.


Cite as: Bellegarda, J.R., Silverman, K.E.A. (2000) Toward unconstrained command and control: data-driven semantic inference. Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000), vol. 1, 258-261

@inproceedings{bellegarda00_icslp,
  author={Jerome R. Bellegarda and Kim E. A. Silverman},
  title={{Toward unconstrained command and control: data-driven semantic inference}},
  year=2000,
  booktitle={Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000)},
  pages={vol. 1, 258-261}
}