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arXiv · 2604.12389

VoxEffects: A Speech-Oriented Audio Effects Dataset and Benchmark

Zhe Zhang·Yigitcan Özer·Junichi Yamagishi
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Abstract

Speech audio in the wild is often processed by post-production effects, but existing speech datasets rarely provide precise annotations of effects and parameters, limiting systematic study. We introduce VoxEffects, a speech audio effects dataset that pairs produced speech with exact effect-chain supervision at multiple granularities. VoxEffects supports speech-oriented audio effect identification: given a produced waveform, infer which effects are present and how they are applied. Built from minimally edited clean speech, it provides an extensible rendering pipeline for both offline synthesis and on-the-fly rendering for efficient training and evaluation. The audio effect identification benchmark includes effect presence detection, preset classification, and intensity prediction, with a robustness protocol covering capture-side and platform-side degradations. We provide an AudioMAE-based multi-task baseline and analyses of domain shift, robustness, input duration, and gender fairness.

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Zhe Zhang, Yigitcan Özer, Junichi Yamagishi. 2026-04-14. VoxEffects: A Speech-Oriented Audio Effects Dataset and Benchmark. https://arxiv.org/abs/2604.12389

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