UBD leverages ensemble uncertainty to estimate per-sample memorization and construct debiased targets for post-hoc correction or unlearning, yielding output distributions closer to uncontaminated models on MMLU-Pro and MATH-MCQA than baselines.
Speech Unlearning
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abstract
We introduce machine unlearning for speech tasks, a novel and underexplored research problem that aims to efficiently and effectively remove the influence of specific data from trained speech models without full retraining. This has important applications in privacy preservation, removal of outdated or noisy data, and bias mitigation. While machine unlearning has been studied in computer vision and natural language processing, its application to speech is largely unexplored due to the high-dimensional, sequential, and speaker-dependent nature of speech data. We define two fundamental speech unlearning tasks: sample unlearning, which removes individual data points (e.g., a voice recording), and class unlearning, which removes an entire category (e.g., all data from a speaker), while preserving performance on the remaining data. Experiments on keyword spotting and speaker identification demonstrate that unlearning speech data is significantly more challenging than unlearning image or text data. We conclude with key future directions in this area, including structured training, robust evaluation, feature-level unlearning, broader applications, scalable methods, and adversarial robustness.
years
2026 2representative citing papers
A system-first taxonomy and literature synthesis of multimodal unlearning across vision, language, video, and audio, with datasets, benchmarks, metrics, applications, and open challenges.
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Uncertainty-based Debiasing and Unlearning for Decontamination
UBD leverages ensemble uncertainty to estimate per-sample memorization and construct debiased targets for post-hoc correction or unlearning, yielding output distributions closer to uncontaminated models on MMLU-Pro and MATH-MCQA than baselines.
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Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks
A system-first taxonomy and literature synthesis of multimodal unlearning across vision, language, video, and audio, with datasets, benchmarks, metrics, applications, and open challenges.