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This paper describes the UCL system for the cognitive load task of the Interspeech 2014 Computational Paralinguistics Challenge. The UCL system evaluates whether additional voice features computed by the VOQAL voice analysis toolbox improves performance over the baseline feature set. 144 different system configurations are evaluated on the development test set, with some systems achieving 100% classification accuracy of cognitive load in the two Stroop subtasks. The difficulty of the reading span sub-task is shown to be caused in part by the duration of the audio material. Performance of the best systems on the test set confirm the importance of building speaker dependent systems. While the VOQAL augmented features gave the best performance on the development test set, no benefit was found for the test set.