Alzheimer's disease (AD) is a progressive disorder that gradually affects memory, language, and reasoning, making early detection crucial for timely intervention. Traditional methods, like medical imaging and clinical evaluations, are costly and limit accessibility. To address this, we propose a speech-based AD detection method that leverages a co-attention mechanism to integrate multilevel acoustic and transcribed text features. These acoustic features include spectrograms, MFCC features, and wav2vec2 embeddings. The mechanism dynamically assigns weights to different features, enhancing their interaction and improving fusion. We also compare early and late fusion strategies to optimize integration. Tested on the ADReSSo dataset, our model achieves 83.15% accuracy, demonstrating the effectiveness of a well-structured integration of acoustic and transcribed text features for accessible and cost-efficient AD detection.