A LIGHTWEIGHT DUAL-STAGE FRAMEWORK FOR PERSONALIZED SPEECH ENHANCEMENT BASED ON DEEPFILTERNET2
Résumé
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.
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