Name
Abstract | ||
|---|---|---|
Face-to-face meetings usually encompass several modalities including sp eech, gesture, handwriting, and person identification. Recognition and integration of each of these modalities is impo rtant to create an accurate record of a meeting. However, each of these modalities presents recognition dif ficulties. Speech recognition must be speaker and domain independent, have low word error rates, and be close to rea l time to be useful. Gesture and handwriting recognition must be writer independent and support a wide vari ety of writing styles. Person identification has difficulty with segmentation in a crowded room. Furthermore, in order to produce the record automatically, we have to solve the assignment problem (who is saying what), which invo lves people identification and speech recognition. We follow a multimodal approach for people identification to increase the robu stness (with the modules: color appearance id, face id and speaker id). This paper will examine a meetin g room system under development at Carnegie Mellon University that enables us to track, capture and in tegrate the important aspects of a meeting from people identification to meeting transcription. Once a multimedia meeting r ecord is created, it can be archived for later retrieval. This paper will review our meeting browser that we have developed which facilitates tracking and reviewing meetings. |
Year | Venue | Keywords |
|---|---|---|
2000 | RIAO | word error rate,assignment problem,handwriting recognition,speech recognition |
Field | DocType | Citations |
Modalities,Handwriting,Intelligent character recognition,Segmentation,Gesture,Computer science,Handwriting recognition,Speech recognition,Robustness (computer science),Speaker recognition | Conference | 29 |
PageRank | References | Authors |
3.08 | 13 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Bett | 1 | 170 | 19.15 |
| Ralph Gross | 2 | 29 | 3.08 |
| Hua Yu | 3 | 30 | 4.11 |
| Xiaojin Zhu | 4 | 3586 | 222.74 |
| Yue Pan | 5 | 29 | 3.08 |
| Jie Yang | 6 | 2856 | 270.24 |
| Alex Waibel | 7 | 6343 | 1980.68 |