Automatic syllable stress detection is useful in assessing and diagnosing the quality of the pronunciation of second language (L2) learners in an automated way. Typically, the syllable stress depends on three prominence measures -- intensity level, duration, pitch -- around the sound unit with the highest sonority in the respective syllable. Stress detection is often formulated as a binary classification task using cues from the feature contours representing the prominence measures. We observe that cues from a feature contour obtained by incorporating relative sonority levels in the prominence measures are more indicative of the syllable stress compared to those from the feature contours representing only the prominence measures. Based on this observation, we propose a new feature contour based on temporal correlation selected sub-band correlation with an optimal set of sub-bands, called sonorous sub-bands, to maximize the stress detection accuracy. Experiments on ISLE corpus show that, for German and Italian non-native English speakers, the syllable stress detection accuracies (87.53% and 86.26%) are higher when the proposed features are used compared to the baseline accuracies (85.81% and 83.17%) indicating the effectiveness of the sonority based prominence features.