Using parallel MLPs as labelers for multiple codebook HMMs

The authors investigate the use of multilayer perceptrons (MLPs) as labelers for a discrete parameter hidden Markov model (HMM) system. They introduce a number of strategies, of which the multi-MLP approach, which uses parallel MLPs for separate parameter sets, is the most promising. The performance of the new system is just as good as that of a classical discrete parameter HMM system (using multiple Euclidean vector quantization), but needs fewer HMM parameters (80 compared with 330 per state). Therefore, multi-MLP labeling is much more efficient than Euclidean labeling.<>