An error correction approach based on the MAP algorithm combined with hidden Markov models

Abstract

An error correction approach which is based on a hidden Markov model (HMM) is proposed. The occurrence probability of a code sequence, which is delivered by the HMMs, is used as the measure for the maximum a posteriori probability (MAP) algorithm. The MAP algorithm is based on the assumption that the source is a discrete-time finite-state Markov process, and the HMM which models a Markov source is well suited for speech data. Therefore this combination would be useful for a speech coding system. The proposed approach is adapted to the code sequence quantized line spectrum frequency (LSF) parameters. When the code sequence is sent over a binary symmetry channel (BSC), the proposed approach with 16-state HMMs improves in code error rate and degradation of the cepstrum distortion by about 27% and 39% respectively for 3% random errors.