ISCA Archive ASR 2000
ISCA Archive ASR 2000

Improved ROVER using language model information

Holger Schwenk, Jean-Luc Gauvain

In the standard approach to speech recognition, the goal is to find the sentence hypothesis that maximizes the posterior probability of the word sequence given the acoustic observation. Usually speech recognizers are evaluated by measuring the word error so that there is a mismatch between the training and the evaluation criterion. Recently, algorithms for minimizing directly the word error and other task specific error criterions have been proposed. This paper presents an extension of the ROVERalgorithm for combining outputs of multiple speech recognizers using both a word error criterion and a sentence error criterion. The algorithm has been evaluated on the 1998 and 1999 broadcast news evaluation test sets, as well as the SDR 1999 speech recognition 10 hour subset and consistently outperformed the standard ROVER algorithm. The approach seems to be of particular interest for improving the recognition performance by combining only two or three speech recognizers achieving relative performance improvements of up to 20% compared to the best single recognizer.

Cite as: Schwenk, H., Gauvain, J.-L. (2000) Improved ROVER using language model information. Proc. ASR2000 - Automatic Speech Recognition: Challenges for the New Millenium, 47-52

  author={Holger Schwenk and Jean-Luc Gauvain},
  title={{Improved ROVER using language model information}},
  booktitle={Proc. ASR2000 - Automatic Speech Recognition: Challenges for the New Millenium},