Extended cluster information vector quantization (ECI-VQ) for robust classification

This paper presents a novel extension to vector quantization referred to as extended cluster information (ECI). In this method the decoder retains more general statistics about the VQ clusters found during codebook training than the single prototypical point of conventional VQ systems. Typically this information is unnecessary, however if the items being compressed are feature space vectors used as input to a statistical pattern classification system, the extra probabilistic information can be used during the classification as in Bayes predictive classification (BPC) to improve recognition results. To demonstrate ECI-VQ, a simple experiment is described where the Aurora2 distributed speech recognition front end is altered to provide more aggressive mel frequency cepstral coefficient (MFCC) compression. As the bit-rate drops, the corresponding recognition performance suffers. It is then shown that using ECI-VQ as the input to an uncertain observation (UO) speech recognizer, a number of errors due to compression can be corrected with no extra cost in bit-rate.