Optimal quantization and bit allocation for compressing large discriminative feature space transforms
Etienne Marcheret, Vaibhava Goel, Peder A. Olsen · 2009
Discriminative training of the feature space using the minimum phone error (MPE) objective function has been shown to yield remarkable accuracy improvements. These gains, however, come at a high cost of memory required to store the transform. In a previous paper we reduced this memory requirement by 94% by quantizing the transform parameters. We used dimension dependent quantization tables and learned the quantization values with a fixed assignment of transform parameters to quantization values. In this paper we refine and extend the techniques to attain a further 35% reduction in memory with no degradation in sentence error rate. We discuss a principled method to assign the transform parameters to quantization values. We also show how the memory can be gradually reduced using a Viterbi algorithm to optimally assign variable number of bits to dimension dependent quantization tables. The techniques described could also be applied to the quantization of general linear transforms - a problem that should be of wider interest.