Approaches to topic identification on the switchboard corpus

Author

McDonough, J. ; Ng, K. ; Jeanrenaud, P. ; Gish, H. ; Rohlicek, J.R.

Author_Institution

BNN Syst. & Technol., Cambridge, MA, USA

Volume

i

fYear

1994

fDate

19-22 Apr 1994

Abstract

Topic identification (TID) is the automatic classification of speech messages into one of a known set of possible topics. The TID task can be view as having three principal components: 1) event generation, 2) keyword event selection, and 3) topic modeling. Using data from the Switchboard corpus, the authors present experimental results for various approaches to the TID problem and compare the relative effectiveness of each. In addition, they examine the effect of keyword set size on identification accuracy and gauge the loss in performance when mismatched topic modeling and keyword selection schemes are used

Keywords

identification; speech processing; speech recognition; TID problem; automatic classification; event generation; identification accuracy; keyword event selection; keyword selection schemes; keyword set size; performance; speech messages; switchboard corpus; topic identification; topic modeling; Air traffic control; Data mining; Event detection; Feature extraction; Hidden Markov models; Natural languages; Performance loss; Speech recognition; Vocabulary;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on

Conference_Location

Adelaide, SA

ISSN

1520-6149

Print_ISBN

0-7803-1775-0

Type

conf

DOI

10.1109/ICASSP.1994.389275

Filename

389275