Name
Abstract | ||
|---|---|---|
When deciding whether to adapt relevant aspects of the system to the particular needs of older users, spoken dialogue systems often rely on automatic detection of chronological age. In tins paper, we show that, vocal ageing as measured by acoustic features is an unreliable indicator of the need for adaptation. Simple lexical features greatly improve the prediction of both relevant, aspects of cognition and interactions style. Lexical features also boost age group prediction. We suggest that adaptation should be based on observed behaviour, not, on chronological age, unless it is not feasible to build classifiers for relevant adaptation decisions. |
Year | Venue | Keywords |
|---|---|---|
2009 | INTERSPEECH 2009: 10TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2009, VOLS 1-5 | age recognition, pitch, keyword spotting, cognitive ageing |
Field | DocType | Citations |
Computer science,Keyword spotting,Speech recognition,Natural language processing,Artificial intelligence,Cognition | Conference | 5 |
PageRank | References | Authors |
0.50 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maria Wolters | 1 | 168 | 15.72 |
| Ravichander Vipperla | 2 | 63 | 6.16 |
| Steve Renals | 3 | 2570 | 293.02 |