Name
Abstract | ||
|---|---|---|
Segmental models are an alternative to frame-based models for sequence prediction, where hypothesized path weights are based on entire segment scores rather than a single frame at a time. Neural segmental models are segmental models that use neural network-based weight functions. Neural segmental models have achieved competitive results for speech recognition, and their end-to-end training has bee... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/JSTSP.2017.2752462 | IEEE Journal of Selected Topics in Signal Processing |
Keywords | DocType | Volume |
Hidden Markov models,Computational modeling,Automatic speech recognition,Speech recognition,Predictive models,Mel frequency cepstral coefficient | Journal | 11 |
Issue | ISSN | Citations |
8 | 1932-4553 | 4 |
PageRank | References | Authors |
0.46 | 31 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao Tang | 1 | 43 | 5.30 |
| Liang Lu | 2 | 894 | 165.81 |
| Lingpeng Kong | 3 | 239 | 17.09 |
| Kevin Gimpel | 4 | 1545 | 79.71 |
| Karen Livescu | 5 | 1254 | 71.43 |
| chris dyer | 6 | 5438 | 232.28 |
| Noah A. Smith | 7 | 5867 | 314.27 |
| Steve Renals | 8 | 2570 | 293.02 |