Dysarthric speech recognition is challenging due to speech variability caused by neurological disorders. This study explores integrating articulatory features with large pre-trained acoustic model features (e.g., WavLM, Whisper) to improve recognition performance. Different fusion strategies, including concatenation and cross-attention mechanisms, are also compared in this work. Experimental results show that articulatory features can enhance WavLM-extracted features, reducing WER for moderate and mild severity level. t-SNE analysis reveal how articulatory features influence feature representations. These findings highlight the potential of multimodal fusion in improving dysarthric ASR systems.