Measuring the quality of noisy speech signals has been an increasingly important problem in the field of speech processing as more and more speech-communication and human-machine-interface systems are deployed in practical applications. In this paper, we study four widely used classical performance measures: signal-to-distortion ratio (SDR), short-time objective intelligibility (STOI), signal-to-noise ratio (SNR), and perceptual evaluation of speech quality (PESQ). Through analyzing these performance measures under the same framework and identifying the relationship between their core parameters, we convert these measures into the corresponding equivalent SNRs. This conversion enables not only some new insights into different quality measures but also a way to combine these measures into a new metric. In the derivation of the equivalent SNRs, we introduce the widely used masking technique into the computation of correlation coefficients, which is subsequently used to analyze STOI. Furthermore, we propose an attention method to compute the core parameters of PESQ, and also an empirical formula to project the equivalent SNRs into PESQ scores. Experiments are carried out and the results justifies the properties of the derived quality measures. •We reveal the relationship of the four well-known speech quality measures, which are originally defined in different domain from different perspective.•We propose a global SNR which can reflect the quality of the speech signal from the perspective of SNR, SDR, STOI, and the PESQ.•We propose a masked correlation coefficient, which can be seen as a generalization of the Pearson correlation coefficient.•We build the relationship between the SNR and the PESQ, propose a formula which can covert one to the other.