Name
Abstract | ||
|---|---|---|
Traditional menu-driven speech recognition systems force users to learn the machine's jargon, but many people can't or won't navigate such highly structured interactions. AT&T's "How May I Help You?" technology shifts the burden to the machine by requiring it to adapt to human language and understand what people actually say rather than what a system designer expects them to say. For a given task, it is more crucial to recognize and understand some linguistic events than others. The authors have developed algorithms that automatically learn the salient words, phrases, and grammar fragments for a given task far more reliably than other methods. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/MC.2002.993771 | IEEE Computer |
Keywords | DocType | Volume |
traditional menu-driven speech recognition,linguistic event,grammar fragment,technology shift,salient word,systems force user,human language,Automated Natural Spoken Dialog,system designer | Journal | 35 |
Issue | ISSN | Citations |
4 | 0018-9162 | 25 |
PageRank | References | Authors |
1.99 | 10 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Allen L. Gorin | 1 | 369 | 59.37 |
| Alicia Abella | 2 | 403 | 47.32 |
| Tirso Alonso | 3 | 53 | 5.07 |
| Giuseppe Riccardi | 4 | 1046 | 101.15 |
| Jeremy H. Wright | 5 | 217 | 29.44 |