10th Annual Conference of the International Speech Communication Association

Brighton, United Kingdom
September 6-10, 2009

Automatic Estimation of Decoding Parameters Using Large-Margin Iterative Linear Programming

Brian Mak, Tom Ko

Hong Kong University of Science & Technology, China

The decoding parameters in automatic speech recognition grammar factor and word insertion penalty are usually determined by performing a grid search on a development set. Recently, we cast their estimation as a convex optimization problem, and proposed a solution using an iterative linear programming algorithm. However, the solution depends on how well the development data set matches with the test set. In this paper, we further investigates an improvement on the generalization property of the solution by using large margin training within the iterative linear programming framework. Empirical evaluation on the WSJ0 5K speech recognition tasks shows that the recognition performance of the decoding parameters found by the improved algorithm using only a subset of the acoustic model training data is even better than that of the decoding parameters found by grid search on the development data, and is close to the performance of those found by grid search on the test set.

Full Paper

Bibliographic reference.  Mak, Brian / Ko, Tom (2009): "Automatic estimation of decoding parameters using large-margin iterative linear programming", In INTERSPEECH-2009, 1219-1222.