one or more vocabularies 113 the asr front end 110 produces feature vectors 111 the feature vectors are used by the fmpe engine 120 as described below in further detail to produce enhanced feature vectors 121 which are used in conjunction with the acoustic model language vocabulary 113 by the asr back end 130 to produce recognized speech 131 which in one embodiment is provided in the form of text as part of the speech recognition process the system 100 processes quantized feature vectors 111 using an fmpe engine 120 the fmpe engine 120 includes a de quantization engine 122 a predetermined quantized fmpe matrix 117 and an fmpe transformer 124 which in one embodiment comprises matrix multiply operations the de quantization engine 122 uses the predetermined quantized fmpe matrix 117 to produce full i e dequantized fmpe values 119 the fmpe transformer 124 uses the full fmpe values 119 to produce the enhanced feature vectors 121 which are integrated with an acoustic model to recognize the speech 102 functionality associated with de quantization engine 122 will now be discussed via diagrams pseudo code and flowchart in fig2 through fig5 for purposes of the following discussion the de quantization engine 122 or other appropriate entity performs steps in the flowcharts referring now to fig2 an exemplary de quantization engine 122 includes a vector processor 126 having storage for different data types which can be operated on by the vector processor 126 the storage includes a first set.