Name
Abstract | ||
|---|---|---|
We define the task of incremental or 0- lag utterance segmentation, that is, the task of segmenting an ongoing speech recog- nition stream into utterance units, and present first results. We use a combination of hidden event language model, features from an incremental parser, and acous- tic / prosodic features to train classifiers on real-world conversational data (from the Switchboard corpus). The best classifiers reach an F-score of around 56%, improv- ing over baseline and related work. |
Year | Venue | Keywords |
|---|---|---|
2008 | COLING (Posters) | language model |
Field | DocType | Volume |
Market segmentation,Pattern recognition,Segmentation,Computer science,Utterance,Speech recognition,Artificial intelligence,Natural language processing,Parsing,Language model | Conference | C08-2 |
Citations | PageRank | References |
9 | 0.94 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michaela Atterer | 1 | 138 | 12.77 |
| Timo Baumann | 2 | 207 | 25.64 |
| david schlangen | 3 | 496 | 54.29 |