Name
Abstract | ||
|---|---|---|
Solving real-world classification and recognition problems requires a principled way of modeling the physical phenomena generating the observed data and the uncertainty in it. The uncertainty originates from the fact that many data generation aspects are influenced by nondirectly measurable variables or are too complex to model and hence are treated as random fluctuations. For example, in speech p... |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/MSP.2012.2208663 | IEEE Signal Processing Magazine |
Keywords | Field | DocType |
Automatic speech recognition,Speech recognition,Uncertainty,Learning systems,Machine learning,Acoustics,Computational modeling,Data models,Hidden Markov models | Speech processing,Speech analytics,Inference,Computer science,Speech recognition,Speaker recognition,Feature (machine learning),Artificial intelligence,Speech production,Machine learning,Test data generation,Acoustic model | Journal |
Volume | Issue | ISSN |
29 | 6 | 1053-5888 |
Citations | PageRank | References |
33 | 1.05 | 32 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tara N. Sainath | 1 | 3497 | 232.43 |
| Bhuvana Ramabhadran | 2 | 1779 | 153.83 |
| David Nahamoo | 3 | 907 | 452.13 |
| Dimitri Kanevsky | 4 | 477 | 54.37 |
| Dirk Van Compernolle | 5 | 393 | 54.05 |
| Kris Demuynck | 6 | 433 | 50.53 |
| Jort F. Gemmeke | 7 | 405 | 28.98 |
| Jerome R. Bellegarda | 8 | 573 | 81.22 |
| Shiva Sundaram | 9 | 142 | 16.01 |