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. 26. The method as in claim 23 wherein the FMQ includes codebooks for each of u speech recognition system vocabulary words.