A Lightweight Dual-Stage Framework for Personalized Speech Enhancement Based on Deepfilternet2

Isolating the desired speaker's voice amidst multiple speakers in a noisy acoustic context is a challenging task. Personalized speech enhancement (PSE) endeavours to achieve this by leveraging prior knowledge of the speaker's voice. Recent research efforts have yielded promising PSE models, albeit often accompanied by computationally intensive architectures, unsuitable for resource-constrained embedded devices. In this paper, we introduce a novel method to personalize 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 architecture. 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.