Maximum likelihood multiple subspace projections for hidden Markov models

The first stage in many pattern recognition tasks is to generate a good set of features from the observed data. Usually, only a single feature space is used. However, in some complex pattern recognition tasks the choice of a good feature space may vary depending on the signal content. An example is in speech recognition where phone dependent feature subspaces may be useful. Handling multiple subspaces while still maintaining meaningful likelihood comparisons between classes is a key issue. This paper describes two new forms of multiple subspace schemes. For both schemes, the problem of handling likelihood consistency between the various subspaces is dealt with by viewing the projection schemes within a maximum likelihood framework. Efficient estimation formulae for the model parameters for both schemes are derived. In addition, the computational cost for their use during recognition are given. These new projection schemes are evaluated on a large vocabulary speech recognition task in terms of performance, speed of likelihood calculation and number parameters