Log-linear optimization of second-order polynomial features with subsequent dimension reduction for speech recognition
Muhammad Ali Tahir, Ralf Schlüter, Hermann Ney · 2011
Second order polynomial features are useful for speech recognition because they can be used to model class specific covariance even with a pooled covariance acoustic model.Previous experiments with second order features have shown word error rate improvements.However, the improvement comes at the price of a large increase in the number of parameters.This paper investigates the discriminative training of second order features, with a subsequent dimension reduction transform to limit the increase in number of parameters.The acoustic model parameters and the transformation matrix parameters are modeled log-linearly and optimized using maximum mutual information criterion.The advantage of log-linear optimization lies in its ability to robustly combine different kinds of features.Experiments are performed for second order MFCC features on the EPPS large vocabulary task and have resulted in a decrease in word error rate.