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arXiv · 2404.08022

A lightweight dual-stage framework for personalized speech enhancement based on DeepFilterNet2

Thomas Serre·Mathieu Fontaine·Éric Benhaim·Geoffroy Dutour·Slim Essid
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Abstract

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.

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Thomas Serre, Mathieu Fontaine, Éric Benhaim, Geoffroy Dutour, Slim Essid. 2024-04-11. A lightweight dual-stage framework for personalized speech enhancement based on DeepFilterNet2. https://arxiv.org/abs/2404.08022

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