Oscillation-based neuro-computational models of speech perception are grounded in the capacity of human brain oscillations to track the speech signal. Consequently, one would expect this tracking to be more efficient for more regular signals. In this paper, we address the question of the contribution of isochrony to event detection by neuro-computational models of speech perception. We consider a simple model of event detection proposed in the literature, based on oscillatory processes driven by the acoustic envelope, that was previously shown to efficiently detect syllabic events in various languages. We first evaluate its performance in the detection of syllabic events for French, and show that "perceptual centers" associated to vowel onsets are more robustly detected than syllable onsets. Then we show that isochrony in natural speech improves the performance of event detection in the oscillatory model. We also evaluate the model's robustness to acoustic noise. Overall, these results show the importance of bottom-up resonance mechanism for event detection; however, they suggest that bottom-up processing of acoustic envelope is not able to perfectly detect events relevant to speech temporal segmentation, highlighting the potential and complementary role of top-down, predictive knowledge.