| P11-1074 | kinds of training methods for | prosodic modeling | . The first one is a supervised |
| H01-1051 | adaptation and robustness techniques . | Prosodic modeling | . Finally , we plan to study |
| E14-2019 | semantic structural analysis and | prosodic modeling | . We do a dependency mapping |
| W12-1615 | work include adding realistic | prosodic modeling | and estimating model parameters |
| W02-0718 | both voice quality and prosody . | Prosodic modeling | is probably the domain from which |
| W04-3209 | training data . For example , | prosodic modeling | assumes acoustic data ; whereas |
| J01-1002 | was not justified . Second , for | prosodic modeling | , we used information from the |
| J01-1002 | for combining the models . 3.1 | Prosodic Modeling | The job of the prosodic model |
| J01-1002 | probability estimators . As in past | prosodic modeling | work ( Shriberg , Bates , and |
| W04-3209 | glossing over some details on | prosodic modeling | that are orthogonal to the discussion |
| J00-3003 | . Computational approaches to | prosodic modeling | of DAs have aimed to automatically |
