Improved quantization structures using generalized HMM modelling with application to wideband speech coding
Ethan R. Duni, A.D. Subramaniam, Bhaskar D. Rao · 2004
In this paper, a low-complexity, high-quality recursive vector quantizer based on a generalized hidden Markov model of the source is presented. Capitalizing on recent developments in vector quantization based on Gaussian mixture models, we extend previous work on HMM-based quantizers to the case of continuous vector-valued sources, and also formulate a generalization of the standard HMM. This leads us to a family of parametric source models with very flexible modelling capabilities, with which are associated low-complexity recursive quantization structures. The performance of these schemes is demonstrated for the problem of wideband speech spectrum quantization, and shown to compare favorably to existing state-of-the-art schemes.