1. A method comprising: representing arc costs associated with at least one finite-state transducer (FST) in fixed-point by converting floating-point costs of the at least one FST to fixed-point by quantization to yield fixed-point FST arc costs; representing parameters associated with a hidden Markov model (HMM) in fixed-point to yield fixed-point HMM parameters; and processing speech data via a processor in a speech recognition decoder using fixed-point arithmetic for the fixed-point FST arc costs and the fixed-point HMM parameters, wherein at least the fixed-point HMM parameters are based on a Mahalanobis distance calculation, wherein a format for the fixed-point FST arc costs and the fixed-point HMM parameters is type Q-2e, wherein Q-2e is a fixed-point format with 2e bits representing a fractional part and e is a normalized error, and wherein the Mahalanobis distance calculation comprises computing Mahalanobis integer products for each Gaussian mean component of the parameters associated with the HMM, performing a right arithmetic shift for each of the Mahalanobis integer products to yield shifted integer products, and summing the shifted integer products.