|
ABSTRACT
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.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
| |
1
|
CNN online news, http://www.cnn.com, 2004.
|
| |
2
|
Resource.com, http://thesaurus.reference.com, 2004.
|
 |
3
|
|
| |
4
|
Bellegarda, J. R., Butzberger, J. W., Chow, Y. L., Coccaro, N. and Naik, D., Automatic Discovery of Word Classes Through Latent Semantic Analysis, in EUSIPCO-96 Signal Processing VIII, Theories and Applications, (1996), Edizioni Lint Trieste.
|
| |
5
|
Bellegarda, J. R., Butzberger, J. W., Chow, Y. L., Coccaro, N. and Naik, D., A Novel Word Clustering Algorithm Based on Latent Semantic Analysis, in ICASSP-96, (1996), 172--175.
|
| |
6
|
Burgess, C., Livesay, K. and Lund, K. Explorations in Context Space: Words, Sentences, Discourse. Discourse Processes, 25. 211--257.
|
| |
7
|
Deerwester, S., Dumais, S. T., Fumas, G. W., Landaur, T. K. and Harshman, R. Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41. 391--407.
|
| |
8
|
Dumais, S. T. Latent semantic indexing (LSI) and TREC-2. in Harman, D. ed. National Institute of Standards and Technology Text Retrieval Conference, NIST, 1994.
|
| |
9
|
|
| |
10
|
Khudanpur, S. and Wu, J., A maximum entropy language model integrating n-grams and topic dependencies for conversational speech recognition. in ICASSP-99, (Phoenix, AZ, 1999), 553--556.
|
| |
11
|
|
| |
12
|
Landaur, T. K. and Dumais, S. T. A solution to Plato's problem: The Latent Semantic Analysis theory of the acquisition, induction, and representation of knowledge. Psychological Review, 104. 211--240.
|
| |
13
|
Rea, L. Designing and Conducting Survey Research. Jossey-Bass, 1997.
|
| |
14
|
Siivola, V., Language modeling based on neural clustering of words, in IDIAP-Com 02, (Martigny, Switzerland, 2000).
|
|