Name
Abstract | ||
|---|---|---|
The use of huge databases in ASR has become an important source of ASR system improvements in the last years. How- ever, their use demands an increase of the computational re- sources necessary to train the recognizers. Several techniques have been proposed in the literature with the purpose of making a better use of these enormous databases by selecting the most 'informative' portions and thus reducing the computational bur- den. In this paper, we present a technique to select samples from a database that allows us to obtain similar results in MLP-based feature extraction stages by using around 60% of the data. |
Year | Venue | Keywords |
|---|---|---|
2005 | INTERSPEECH | feature extraction |
Field | DocType | Citations |
Pattern recognition,Data selection,Computer science,Speech recognition,Feature extraction,Artificial intelligence | Conference | 1 |
PageRank | References | Authors |
0.35 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carmen Peláez-moreno | 1 | 130 | 22.07 |
| Qi-Feng Zhu | 2 | 340 | 34.69 |
| Barry Y. Chen | 3 | 203 | 22.31 |
| Nelson Morgan | 4 | 3048 | 533.52 |