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High-Rate Optimized Recursive Vector Quantization Structures Using Hidden Markov Models

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2 Author(s)
Ethan R. Duni ; Dept. of Electr. & Comput. Eng., California Univ., San Diego, La Jolla, CA ; Bhaskar D. Rao

This paper examines the design of recursive vector quantization systems built around Gaussian mixture vector quantizers. The problem of designing such systems for minimum high-rate distortion, under input-weighted squared error, is discussed. It is shown that, in high dimensions, the design problem becomes equivalent to a weighted maximum likelihood problem. A variety of recursive coding schemes, based on hidden Markov models are presented. The proposed systems are applied to the problem of wideband speech line spectral frequency (LSF) quantization under the log spectral distortion (LSD) measure. By combining recursive quantization and random coding techniques, the systems are able to attain transparent quality at rates as low as 36 bits per frame

Published in:

IEEE Transactions on Audio, Speech, and Language Processing  (Volume:15 ,  Issue: 3 )