Learning improved linear transforms for speech recognition

This paper explores a large margin approach to learning a linear transform for
dimensionality reduction. The method assumes a trained Gaussian mixture model
for the each class to be discriminated and trains a linear transform with
respect to the model using stochastic gradient descent. Results are presented
showing improvements in state classification for individual frames and reduced
word error rate in a large vocabulary speech recognition problem after maximum likelihood training and boosted maximum mutual information training.

• Speech Processing