8th International Conference on Spoken Language Processing

Jeju Island, Korea
October 4-8, 2004

A Frame Level Boosting Training Scheme for Acoustic Modeling

Rong Zhang, Alexander Rudnicky

Carnegie Mellon University, USA

Conventional Boosting algorithms for acoustic modeling have two notable weaknesses. (1) The objective function aims to minimize utterance error rate, though the goal for most speech recognition systems is to reduce word error rate. (2) During Boosting training, an utterance is treated as a unit for re-sampling and each frame within the same utterance is assigned equal weight. Intuitively, the frames associated with a misclassified word should be given more emphasis than others. We propose a frame level Boosting training scheme that addresses these shortcomings and allows each frame to have a different weight. We describe a technique and provide experimental results for this approach.

Full Paper

Bibliographic reference.  Zhang, Rong / Rudnicky, Alexander (2004): "A frame level boosting training scheme for acoustic modeling", In INTERSPEECH-2004, 417-420.