5. The computer-implemented method of claim 1 , wherein computing the third sequence of feature vectors comprises: multiplying a vector of the second sequence of feature vectors by the second linear portion of the NN-based transform to generate a first product; and adding the first product to the second bias portion of the NN-based transform to generate a vector of the third sequence of feature vectors.
1. A computer-implemented method comprising: under control of one or more computing devices configured with specific computer-executable instructions, obtaining a Gaussian mixture model-based (“GMM-based”) acoustic model; obtaining a neural network-based (“NN-based”) acoustic model; receiving an audio signal comprising speech; computing a first sequence of feature vectors from the audio signal; computing a GMM-based transform using the GMM-based acoustic model and the first sequence of feature vectors, wherein the GMM-based transform comprises a first linear portion and a first bias portion; computing a second linear portion of a NN-based transform by minimizing a first least squares difference function, wherein the first least squares difference function comprises a difference between the second linear portion and the first linear portion; computing a second bias portion of the NN-based transform by minimizing a second least squares difference function, wherein the second least squares difference function comprises a difference between the second bias portion and the first bias portion; computing a second sequence of feature vectors from the audio signal; computing a third sequence of feature vectors by applying the second linear portion and the second bias portion of the NN-based transform to the second sequence of feature vectors; performing speech recognition using the third sequence of feature vectors and the NN-based acoustic model generate speech processing results; and determining, using the speech processing results, an action to perform.