Abstract
Conventional error concealment (EC) algorithms for distributed speech recognition (DSR) share a common characteristic namely the fact of conducting EC at the vector (or frame) level. This strategy, however, fails to effectively exploit the error-free fraction left within erroneous vectors where a substantial number of subvectors often are error-free. This paper proposes a novel EC approach for DSR encoded by split vector quantization (SVQ) where the detected erroneous vectors are submitted to a further analysis at the subvector level. Specifically, a data consistency test is applied to each erroneous vector to identify inconsistent subvectors. Only inconsistent subvectors are replaced by their nearest neighbouring consistent subvectors whereas consistent subvectors are kept untouched. Experimental results demonstrate that the proposed algorithm in terms of recognition accuracy is superior to conventional EC methods having almost the same complexity and resource requirement.
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