Confidence measures are expected to give a measure of reliability on the result of a speech/speaker recognition system. Most commonly used confidence measures are based on posterior word or phoneme probabilities which can be obtained from the output of the recognizer. In this paper we introduced a linear interpretation of posterior probability based confidence measure by using inverse Fisher transformation. Speaker adaptation consists in updating model parameters of a speaker independent model to have a better representation of the current speaker. Confidence measures give more reliable selection criteria to select the utterances which best represent the speaker. A linear interpretation of confidence measure is very important to select the most representative data for adaptation.