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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.