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We then applied the minimum phone error (MPE) discriminative training technique described in [17].

Acoustic model training was done using the minimum phone error (MPE) [14] criteria.

First order approximation of the minimum phone error (MPE) objective function is used for question pruning to make tree building efficient.

The GMM-HMM baseline system has 40-Gaussian mixtures per state, trained with maximum likelihood (ML), and refined discriminatively (DT) with the minimum phone error (MPE) criterion.

The resulting DNN (with 5 hidden layers, 1024 neurons in each hidden layer, and 3435 outputs in the output layer) goes through one iteration of sequence training with MPE (minimum phone error) criteria.

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Fig. 5 The progress of phone error rates during training.

Fig. 2 Phone error rate as a function of the pooling size.

We also examine the patterns of phone error reduction and look at the cost-performance tradeoffs.

Fig. 3 Phone error rate as a function of the input context size.

The phone error rates are shown as a function of the pooling size.

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