In the field of Intelligent User Interfaces, Spoken Dialogue Systems (SDSs) play a key role as speech represents a true intuitive means of human communication. Deriving information about its quality can help rendering SDSs more user-Adaptive. Work on automatic estimation of subjective quality usually relies on statistical models. To create those, manual data annotation is required, which may be performed by actual users or by experts. Here, both variants have their advantages and drawbacks. In this paper, we analyze the relationship between user and expert ratings by investigating models which combine the advantages of both types of ratings. We explore two novel approaches using statistical classification methods and evaluate those with a preexisting corpus providing user and expert ratings. After analyzing the results, we eventually recommend to use expert ratings instead of user ratings in general
