Computer Vision And Pattern Analysis Laboratory Home Page  Home
People  People
Publications  Publications
Publications  Databases
Contact Information  Contact
Supported Research Projects  Supported Research Projects
Research Activites  Research Activites
Research Groups
SPIS - Signal Processing and Information Systems Lab.SPIS - Signal Processing and Information Systems Lab.
Medical Vision and Analysis Group  Medical Research Activities
Biometrics Research Group  Biometrics Research Group
SPIS - Signal Processing and Information Systems Lab.MISAM - Machine Intelligence for Speech Audio and Multimedia.
Knowledge Base
  Paper Library
Filler Model Based Confidence Measures for Spoken Dialog Systems (in Turkish)
Authors: A. Akyol, H. Erdogan
Published in: ICASSP 2004
Publication year: 2004
Abstract: Because of the inadequate performance of speech recognition systems, an accurate confidence scoring mechanism should be employed to understand user requests correctly. To determine a confidence score for a hypothesis, certain confidence features are combined. The performance of filler model based confidence features are investigated. Five types of filler model networks were defined: triphone-network, phone-network, phone-class network, 5-state catch-all model and 3-state catch-all model. First, all the models were evaluated in a Turkish speech recognition task in terms of their ability to tag correctly (recognition-error or correct) recognition hypotheses. The best performance was obtained from the triphone recognition network. Then, the performance of reliable combinations of these models was investigated and it was observed that certain combinations of filler models could significantly improve the accuracy of the confidence annotation.
  download full paper

Home Back