Speech enhancement and separation algorithms frequently employ two-stage processing schemes, where the signal is first mapped to an intermediate low-dimensional parametric description. Then, these parameters are mapped to vectors in codebooks trained on individual noise-free sources using a vector quantizer. To obtain accurate parameters, one must employ an estimator that takes the signal characteristics into account. An open question is, however, how to derive metrics for use in the vector quantization process. In this paper, we present and derive a new metric aimed at exactly this, and we exemplify and demonstrate its use in sinusoidal modeling. The metric takes into account that parameters may have different uncertainties and dependencies associated with them and thus leads to more accurate estimates, as is demonstrated in experiments. Moreover, we incorporate the metric in a recently proposed speech separation algorithm and compare its performance to state-of-the-art methods.