학술논문

Learning the decision function for speaker verification
Document Type
Conference
Source
2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221) Acoustics, speech, and signal processing Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP '01). 2001 IEEE International Conference on. 1:425-428 vol.1 2001
Subject
Signal Processing and Analysis
Components, Circuits, Devices and Systems
Hidden Markov models
Linear regression
Support vector machines
Probability
Speech
Equations
Language
ISSN
1520-6149
Abstract
Explores the possibility of replacing the usual thresholding decision rule of log likelihood ratios used in speaker verification systems by more complex and discriminant decision functions based for instance on linear regression models or support vector machines. Current speaker verification systems, based on generative models such as HMMs or Gaussian mixture models, can indeed easily be adapted to use such decision functions. Experiments on both text dependent and text independent tasks always yielded performance improvements and sometimes significantly.