Optimal Clustering and Non-uniform Allocation of Gaussian Kernels in Scalar Dimension for Hmm Compression

We propose an algorithm for optimal clustering and nonuniform allocation of Gaussian Kernels in scalar (feature) dimension to compress complex, Gaussian mixture-based, continuous density HMMs into computationally efficient, small footprint models. The symmetric Kullback-Leibler divergence (KLD) is used as the universal distortion measure and it is minimized in both kernel clustering and allocation procedures. The algorithm was tested on the Resource Management (RM) database. The original context-dependent HMMs can be compressed to any resolution, measured by the total number of clustered scalar kernel components. Good trade-offs between the recognition performance and model complexities have been obtained; HMM can be compressed to 15-20% of the original model size, which needs 1-5% of multiplication/division operations, and results in almost negligible recognition performance degradation.