Efficient second-order adaptation for large vocabulary distributed speech recognition

This paper describes practical implementation details for a second-order approximation to the parallel model combination (PMC) algorithm with application to large vocabulary distributed speech recognition. The proposed method is capable of simultaneously adapting to noise and channel changes. A more accurate method for computing the derivatives based on numeric integration PMC is introduced. The proposed second-order adaptation algorithm requires only twice the memory and computation of standard Jacobian Adaptation (JA). This represents a 382-fold reduction in memory and a 29-fold reduction in computation. Moreover, the proposed algorithm produces models that are much closer to the PMC-derived models than standard JA.