Deep neural networks have dominated speaker recognition, with a sharp increase in performance associated with increasingly complex models. This comes at the cost of transparency, which poses serious problems for informed decision making. In response, an intrinsically interpretable scoring approach, BA-LR, was recently presented. This method uses an attribute-based bottom-up representation of speech, linked with a transparent scoring scheme. For the sake of explainability, the present work adds an analysis of the nature of the attributes, by selecting and quantifying the contributions of the phonetic variables that describe it. We propose two methods based on statistical and surrogate models, respectively. The results reveal that the speech attributes are each well described by a set of descriptive variables. This allows us to propose the first transparent scoring scheme in speaker recognition, where the weights of the phonetic variables contributing to each decision item are known.