Acoustic model transformations based on random projections

This paper proposes a novel acoustic model transformation method for speech recognition based on random projections. Random projections have been suggested as a means of dimensionality reduction, where the original data are projected onto a subspace using a random matrix. Moreover, as we are able to produce various random matrices, it may be possible to find a transform matrix that is superior to conventional transformation matrices among random matrices. In our previous work, a random-projection-based feature combination technique has been proposed but had a high computational cost. In order to deal with this cost, in this paper, we introduce random projections on the acoustic model domain, where linear transformations are applied to an acoustic model using random matrices. Its effectiveness is confirmed by word recognition experiments on noisy speech.