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The Cognitive Load (CL) refers to the load imposed on an individual's cognitive system when performing a given task, and is usually associated with the limitations of the human working memory. Stress, fatigue, lower ability to make decisions and perceptual narrowing are induced by cognitive overload which occurs when too much information has to be processed. As many physiological measures and for a nonintrusive measurement, speech features have been investigated in order to find reliable indicators of CL levels. In this paper, we have investigated high-level speech events automatically detected using the CMU-Sphinx toolkit for speech recognition. Temporal events (speech onset latency, event starting time-codes, pause and phone segments) were extracted from the speech transcriptions (phoneme, word, silent pause, filled pause, breathing). Seven audio feature sets related to the speech events were designed and assessed. Three-class SVM classifiers (Low, Medium and High level) were developed and assessed on the CSLE (Cognitive-Load with Speech and EGG) databases provided for the Interspeech'2014 Cognitive Load Sub-Challenge. These experiments have shown an improvement of 1.5% on the Test set compared to the official baseline Unweighted Average Recall (UAR).