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Speech pauses serve as a valuable and non-invasive biomarker for the early detection of dementia. Our study aims to examine abnormal pauses, specifically their durations, for improving the detection performance. Inspired by the proven performance of the Transformer-based models in dementia detection, we opted for integrating the abnormal pauses into these models. Specifically, we enriched the inputs for the Transformer-based models by fusing between-segment pause context into the automated transcriptions. We performed the experiments on our Cantonese elderly corpus called CU-Marvel. To improve the detection performance, we optimized the pause durations when infusing the pause context into the transcriptions. Our findings suggest that the between-segment pauses could also serve as promising biomarkers. We emphasize the importance of optimizing pause patterns across different languages or datasets. Our findings indicate that various across different languages or datasets.