  Estimating the quality of remote speech communication is a complex task
influenced by the speaker, transmission channel, and listener. For example, the
degradation of transmission quality can increase listeners' cognitive load,
which can influence the overall perceived quality of the conversation. This
paper presents a framework that isolates quality-dependent changes and controls
most outside influencing factors like personal preference in a simulated
conversational environment. The performed statistical analysis finds
significant relationships between stimulus quality and the listener's valence
and personality (agreeableness and openness) and, similarly, between the
perceived task load during the listening task and the listener's personality
and frustration intolerance. The machine learning model of the task load
prediction improves the correlation coefficients from 0.48 to 0.76 when
listeners' individuality is considered. The proposed evaluation framework and
results pave the way for personalized audio quality assessment that includes
speakers' and listeners' individuality beyond conventional channel modeling.
