Objective quality evaluation is widely used in speech coding, yet objective estimates often show limited agreement with subjective listening-test results. Rather than focusing on absolute score accuracy, this paper evaluates objective speech quality models from a decision-making perspective, defined as their ability to support comparative judgments between speech codecs or codec configurations. A formal ITU-R P.800 Absolute Category Rating (ACR) listening test was conducted with 30 listeners across 24 conditions, covering conventional; neural monophonic speech codecs operating under clear-channel conditions at sampling frequencies from 16 to 48 kHz; bit rates ranging from below 1 kbps to above 16 kbps. The speech material consisted of internally recorded, clean French-language speech that was not used in the development or training of any of the evaluated codecs or objective quality models. Seven objective quality models, namely PESQ, VISQOL Speech, VISQOL Audio, WARP-Q, NISQA, UTMOS,; DistillMOS, were evaluated on the same material. Decision-making performance was assessed by comparing subjective; objective rankings using Kendall’s rank correlation coefficient; by analyzing pairwise codec comparisons using t-tests at a 95% confidence level. The results show that some objective quality models are effective for comparing bit rate variations within a given speech coding technology, provided that all other codec parameters remain unchanged (e.g., sampling frequency). However, all models exhibit limitations, including tendencies toward over- or underestimation for certain technologies, as well as reduced reliability when applied across different sampling frequencies. Despite its conventional origins, PESQ remains capable of supporting decision-making even when applied to neural speech codecs.
