Objectives for Discriminative Training

Chapter
Part of the Signals and Communication Technology book series (SCT)

Abstract

The first step in discriminative training is to define an objective function. In this chapter, the relations among a class of discriminative training objectives is derived and discovered through our theoretical analysis. The objectives selected for our discussion are the minimum classification error (MCE), maximum mutual information (MMI), minimum error rate (MER), and generalized minimum error rate (GMER). The author’s analysis shows that all these objectives can be related to both minimum error rates and maximum a posteriori probability. In theory, the MCE and GMER objectives are more general and flexible than the MMI and MER objectives, and MCE and GMER are beyond the Bayesian decision theory. The results and the analytical methods used in this chapter can help in judging and evaluating discriminative objectives, and in defining new objectives for different tasks and better performances. We note that although our discussions are based on the applications of speaker recognition, the analysis can be further extended to speech recognition tasks.

Keywords

Sigmoid Function Equal Error Rate Speaker Recognition Posteriori Probability Speaker Veri 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg  2012

Authors and Affiliations

  1. 1.Li Creative Technologies (LcT), IncFlorham ParkUSA

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