A fast and flexible implementation of parallel model combination

In previous papers the use of parallel model combination (PMC) for noise robustness has been described. Various fast implementations have been proposed, though to date in order to compensate all the parameters of a system it has been necessary to perform Gaussian integration. This paper introduces an alternative method that can compensate all the parameters of the recognition system, whilst reducing the computational load of this task. Furthermore, the technique offers an additional degree of flexibility, as it allows the number of components to be chosen and optimised using standard iterative techniques. The new technique is referred to as data-driven PMC (DPMC). It is evaluated on the Resource Management database, with noise artificially added from the NOISEX-92 database. The performance of DPMC is found to be comparable to PMC, at a far lower computational cost. In complex noise environments, by more accurately modelling the noise source, using multiple components, and then reducing the number of components to the original number a slight improvement in performance is obtained