Isolating the desired speaker’s voice amidst multiple speakers in a noisy acoustic context is a challenging task. Per- sonalized speech enhancement (PSE) endeavours to achieve this by leveraging prior knowledge of the speaker’s voice. Recent research efforts have yielded promising PSE mod- els, albeit often accompanied by computationally intensive architectures, unsuitable for resource-constrained embedded devices. In this paper, we introduce a novel method to per- sonalize a lightweight dual-stage Speech Enhancement (SE) model and implement it within DeepFilterNet2, a SE model renowned for its state-of-the-art performance. We seek an optimal integration of speaker information within the model, exploring different positions for the integration of the speaker embeddings within the dual-stage enhancement architec- ture. We also investigate a tailored training strategy when adapting DeepFilterNet2 to a PSE task. We show that our personalization method greatly improves the performances of DeepFilterNet2 while preserving minimal computational overhead.