Name
Title | ||
|---|---|---|
A multiple classifier-based concept-spotting approach for robust spoken language understanding |
Abstract | ||
|---|---|---|
Abstract In this paper, we present a concept spotting approach using manifold,machine,learning techniques for robust spoken,lan- guage understanding. The goal of this approach is to find proper values for pre-defined slots of given meaning,representation. Especially we propose a voting-based selection using multiple classifiers for robust spoken language,understanding. This ap- proach proposes no full level of language understanding but par- tial understanding,because the method,is only interested in the pre-defined meaning,representation slots. In spite of this partial understanding, we can acquire necessary information to make interesting applications from the slot values because the slots are properly designed for specific domain-oriented understand- ing tasks. In several experimental results, the SLU (Spoken Language Understanding) performance,degradation of spoken inputs compared with textual inputs are only F-measure 10.72, 11.43 and 11.51 for speech act, main goal and component slot extraction task respectively although the WER of spoken inputs is as high as 18.71%. That is, the evaluation results show that our concept spotting approach for SLU system is especially ro- bust for spoken language input which has large recognition er- rors. |
Year | Venue | Keywords |
|---|---|---|
2005 | INTERSPEECH | machine learning |
Field | DocType | Citations |
Voting,Computer science,Speech recognition,Natural language processing,Artificial intelligence,Classifier (linguistics),Speech act,Spotting,Spoken language,Language understanding | Conference | 4 |
PageRank | References | Authors |
0.44 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jihyun Eun | 1 | 17 | 2.32 |
| Minwoo Jeong | 2 | 142 | 13.89 |
| Gary Geunbae Lee | 3 | 932 | 93.23 |