23. A method of robust speech recognition in an automotive environment comprising the steps of: receiving a speech input signal; representing the speech input signal with a vector .function. of P line spectral pair frequencies; representing a codeword in a quantizer codebook as a vector .function. of P line spectral pair frequencies; and determining a distance measure between the vector .function. and the vector .function., wherein the distance measure, d(.function.,.function.), is defined by: ##EQU29## wherein the constants .alpha..sub.1, .alpha..sub.2, .beta..sub.1 and .beta..sub.2 are set to substantially minimize quantization error, and e.sub.i is the error power spectrum of the speech input signal and a predicted speech input signal at the ith line spectral pair frequency of the speech input signal.
23. A method of robust speech recognition in an automotive environment comprising the steps of: receiving a speech input signal; representing the speech input signal with a vector .function. of P line spectral pair frequencies; representing a codeword in a quantizer codebook as a vector .function. of P line spectral pair frequencies; and determining a distance measure between the vector .function. and the vector .function., wherein the distance measure, d(.function.,.function.), is defined by: ##EQU29## wherein the constants .alpha..sub.1, .alpha..sub.2, .beta..sub.1 and .beta..sub.2 are set to substantially minimize quantization error, and e.sub.i is the error power spectrum of the speech input signal and a predicted speech input signal at the ith line spectral pair frequency of the speech input signal. 24. The method as in claim 23 further comprising the steps of: using the distance measure, d(.function.,.function.), to generate fuzzy distance measures in an fuzzy matrix quantization (FMQ)/hidden Markov model (HMM) speech recognition system.