Diff-SV: A Unified Hierarchical Framework for Noise-Robust Speaker Verification Using Score-Based Diffusion Probabilistic Models

Ju-ho Kim; Jungwoo Heo; Hyun-seo Shin; Chan-yeong Lim; Ha-Jin Yu

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arXiv · 2309.08320

Diff-SV: A Unified Hierarchical Framework for Noise-Robust Speaker Verification Using Score-Based Diffusion Probabilistic Models

Ju-ho Kim·Jungwoo Heo·Hyun-seo Shin·Chan-yeong Lim·Ha-Jin Yu

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Abstract

Background noise considerably reduces the accuracy and reliability of speaker verification (SV) systems. These challenges can be addressed using a speech enhancement system as a front-end module. Recently, diffusion probabilistic models (DPMs) have exhibited remarkable noise-compensation capabilities in the speech enhancement domain. Building on this success, we propose Diff-SV, a noise-robust SV framework that leverages DPM. Diff-SV unifies a DPM-based speech enhancement system with a speaker embedding extractor, and yields a discriminative and noise-tolerable speaker representation through a hierarchical structure. The proposed model was evaluated under both in-domain and out-of-domain noisy conditions using the VoxCeleb1 test set, an external noise source, and the VOiCES corpus. The obtained experimental results demonstrate that Diff-SV achieves state-of-the-art performance, outperforming recently proposed noise-robust SV systems.

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Ju-ho Kim, Jungwoo Heo, Hyun-seo Shin, Chan-yeong Lim, Ha-Jin Yu. 2023-12-14. Diff-SV: A Unified Hierarchical Framework for Noise-Robust Speaker Verification Using Score-Based Diffusion Probabilistic Models. https://arxiv.org/abs/2309.08320

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