שמור ב:
| Main Authors: | , , , , |
|---|---|
| פורמט: | Preprint |
| יצא לאור: |
2024
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| נושאים: | |
| גישה מקוונת: | https://arxiv.org/abs/2404.08022 |
| תגים: |
הוספת תג
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תוכן הענינים:
- Isolating the desired speaker's voice amidst multiplespeakers in a noisy acoustic context is a challenging task. Per-sonalized speech enhancement (PSE) endeavours to achievethis by leveraging prior knowledge of the speaker's voice.Recent research efforts have yielded promising PSE mod-els, albeit often accompanied by computationally intensivearchitectures, unsuitable for resource-constrained embeddeddevices. 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 modelrenowned for its state-of-the-art performance. We seek anoptimal integration of speaker information within the model,exploring different positions for the integration of the speakerembeddings within the dual-stage enhancement architec-ture. We also investigate a tailored training strategy whenadapting DeepFilterNet2 to a PSE task. We show that ourpersonalization method greatly improves the performancesof DeepFilterNet2 while preserving minimal computationaloverhead.