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The perception of syllable prominence depends to a limited extent on the acoustic properties of the speech signal in question. Psychoacoustic factors are involved as well. Thus, research often relies on two types of data: subjective prominence ratings collected in perception experiments and acoustic measures. A problem with the rating data is noise resulting from individual approaches to the rating task. This paper addresses the question of how this noise can be reduced by normalization, evaluating 12 normalization methods. In a perception experiment, prominence ratings concerning German read speech were collected. From the raw rating data 12 different ‘mirror’ data-sets were computed according to the 12 methods. Each mirror data-set was correlated with the same set of underlying acoustic data. The multiple regression setup included raw syllable duration as well as within-syllable maximum F0 and intensity. Adjusted r2-values could beraised considerably with selected methods.
The instructions under which raters quantify syllable prominence perception need to be simple in order to maintain immediate reactions. This leads to noise in the rating data that can be dealt with by normalization, e.g. setting central tendency = 0 and dispersion = 1 (as in Z-score normalization). Questions arise such as: Which parameter is adequate here to capture central tendency? Which reference distribution should the normalization be based on? In this paper 16 different normalization methods are evaluated. In a perception experiment using German read speech (prose and poetry), syllable prominence ratings were collected. From the rating data 16 complete “mirror” data-sets were computed according to the 16 methods. Each mirror data-set was correlated with the same set of measures from the underlying acoustic data, focusing on raw syllable duration which is seen as a rather straightforward acoustic aspect of syllable prominence. Correlation coefficients could be raised considerably by selected methods.