Paper: Data-Driven Classification Of Linguistic Styles In Spoken Dialogues

ACL ID C02-1081
Title Data-Driven Classification Of Linguistic Styles In Spoken Dialogues
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2002
Authors
  • Thomas Portele (Philips Research Laboratories Aachen, Aachen Germany)

Language users have individual linguistic styles. A spo- ken dialogue system may benefit from adapting to the linguistic style of a user in input analysis and output gen- eration. To investigate the possibility to automatically classify speakers according to their linguistic style three corpora of spoken dialogues were analyzed. Several nu- merical parameters were computed for every speaker. These parameters were reduced to linguistically inter- pretable components by means of a principal component analysis. Classes were established from these compo- nents by cluster analysis. Unseen input was classified by trained neural networks with varying error rates depend- ing on corpus type. A first investigation in using special language models for speaker classes was carried out. 1 Motivation Wit...