| Paper: | SP-L7.5 | ||
| Session: | Quantization Techniques in Speech Coding | ||
| Time: | Thursday, May 20, 16:50 - 17:10 | ||
| Presentation: | Lecture | ||
| Topic: | Speech Processing: Wideband Speech Coding | ||
| Title: | IMPROVED QUANTIZATION STRUCTURES USING GENERALIZED HMM MODELLING WITH APPLICATION TO WIDEBAND SPEECH CODING | ||
| Authors: | Ethan Duni; University of California, San Diego | ||
| Anand Subramaniam; University of California, San Diego | |||
| Bhaskar Rao; University of California, San Diego | |||
| Abstract: | In this paper, a low-complexity, high-quality recursive vectorquantizer 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 problemof wideband speech spectrum quantization, and shown to compare favorably to existing state-of-the-art schemes. | ||
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