In this paper, we report our experiments on Interspeech 2012 Speaker Trait Pathology challenge task . Specifically, we investigate two factors that impact the acoustic properties of the utterances collected in this task. Although the task treats utterances as independent data points, multiple utterances are recorded from individual speakers. Furthermore, the utterances correspond to readings of 17 given written sentences. In one experiment, we attempt to reduce variation due to speaker through dimensionality reduction. While these experiments showed promising results on development set, the performance did not translate to the evaluation test. In another, we learn classifiers conditioned on the sentences to capture sentence-specific signatures. This approach showed improved performance over the baseline on development set and the improvement translated to marginal gains on evaluation set. These experiments demonstrates the need to pay attention to the independence assumptions while collecting and defining clinical tasks.