ISCA Archive Interspeech 2008
ISCA Archive Interspeech 2008

Min-max discriminative training of decoding parameters using iterative linear programming

Brian Mak, Tom Ko

In automatic speech recognition, the decoding parameters - grammar factor and word insertion penalty - are usually hand-tuned to give the best recognition performance. This paper investigates an automatic procedure to determine their values using an iterative linear programming (LP) algorithm. LP naturally implements discriminative training by mapping linear discriminants into LP constraints. A min-max cost function is also defined to get more stable and robust result. Empirical evaluations on the RM1 and WSJ0 speech recognition tasks show that decoding parameters found by the proposed algorithm are as good as those found by a brute-force grid search; their optimal values also seem to be independent of the initial values set to start the iterative LP algorithm.


doi: 10.21437/Interspeech.2008-107

Cite as: Mak, B., Ko, T. (2008) Min-max discriminative training of decoding parameters using iterative linear programming. Proc. Interspeech 2008, 915-918, doi: 10.21437/Interspeech.2008-107

@inproceedings{mak08_interspeech,
  author={Brian Mak and Tom Ko},
  title={{Min-max discriminative training of decoding parameters using iterative linear programming}},
  year=2008,
  booktitle={Proc. Interspeech 2008},
  pages={915--918},
  doi={10.21437/Interspeech.2008-107}
}