This paper presents an unsupervised method for produc-ing a bounded rating of affective arousal from speech. One of the major challenges in such behavioral signal classification is the design of methods that generalize well across domains and datasets. We propose a frame-work that provides robustness across databases by: se-lecting coherent features based on empirical and theoret-ical evidence, fusing activation confidences from mul-tiple features, and effectively weighting the soft-labels without knowing the true labels. Spearman’s rank-correlation (and binary classification accuracy) on four