Jon A. Arrowood

Comparison of autoregressive parameter estimation algorithms for speech processing and recognition

Noise mitigation systems for speech coding and recognition have primarily focused on spectral subtraction techniques due to their well understood behavior and computational simplicity. As computation complexity becomes a smaller constraint, understanding the characteristics of different estimation schemes becomes more important. The merits of two algorithms based on direct estimation of the linear prediction spectrum of a speech signal are explored. These algorithms are maximum likelihood (ML) and minimum mean square error estimation (MMSE) of the autoregressive speech spectrum. The MMSE algorithm is able to improve objective quality effectively at low SNRs while also improving the speech recognition accuracy by 20-30% on the Aurora2 test set at the cost of requiring two orders of magnitude more operations than the ML method. Because of these improvements, autoregressive based algorithms should be considered in the future for noise robust speech processing tasks.

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.

Scoring algorithms for wordspotting systems

When evaluating wordspotting systems, one normally compares receiver operating characteristic curves and different measures of accuracy. However, there are many other factors that are relevant to the systems usability for searching speech. In this paper, we discuss both measures of quality for confidence scores and propose algorithms for producing scores that are optimal with respect to these criteria.