Google Research

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.

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