Multimodal Speaker Verification Robustness via Generative Audio Enhancement

Abstract

Abstract: Recent advancements in speaker verification techniques show promise, but their performance often deteriorates significantly in challenging acoustic environments. Although speech enhancement methods can improve perceived audio quality, they may unintentionally distort speaker-specific information, which can affect verification accuracy. This problem has become more noticeable with the increasing use of generative deep neural networks (DNNs) for speech enhancement. While these networks can produce intelligible speech even in conditions of very low signal-to-noise ratio (SNR), they may also sever

Research Question

Does training multimodal speaker verification backbones with generatively enhanced audio improve robustness scores on the AVSpeech noisy test split compared to raw audio baselines?

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Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance