State-of-the-art (SOTA) Automatic Speech Recognition (ASR) systems primarily rely on acoustic information while disregarding additional multi-modal context.However, visual information are essential in disambiguation and adaptation.While most work focus on speaker images to handle noise conditions, this work also focuses on integrating presentation slides for the use cases of scientific presentation.In a first step, we create a benchmark for multimodal presentation including an automatic analysis of transcribing domain-specific terminology.Next, we explore methods for augmenting speech models with multi-modal information.We mitigate the lack of datasets with accompanying slides by a suitable approach of data augmentation.Finally, we train a model using the augmented dataset, resulting in a relative reduction in word error rate of approximately 34%, across all words and 35%, for domainspecific terms compared to the baseline model.Our implementation is available 1 .