提出SISA++方法，实现语音情感与抑郁检测的可撤销学习。
Towards Machine Unlearning for Paralinguistic Speech Processing
- 通过加权平均融合不同数据片模型，改进可撤销学习方法。
- 在CREMA-D和E-DAIC数据集上，未学习后性能下降更少。
- 提供特征与架构选择建议，助力后续研究落地。
本文首次探索语音情感识别（SER）与抑郁症检测（DD）中的机器可撤销学习（MU）。针对这两个关键任务，我们提出SISA++——在现有最优方法SISA基础上，通过加权平均融合不同数据分片训练的模型。实验表明，在基准数据集CREMA-D（SER）和E-DAIC（DD）上，该方法相较SISA能更好保持性能。此外，为促进未来研究采纳，我们提供“操作手册”式建议，包括可有效缓解性能下降的特征表示与下游模型架构选择策略。
原文摘要 · Abstract (English)
In this work, we pioneer the study of Machine Unlearning (MU) for Paralinguistic Speech Processing (PSP). We focus on two key PSP tasks: Speech Emotion Recognition (SER) and Depression Detection (DD). To this end, we propose, SISA++, a novel extension to previous state-of-the-art (SOTA) MU method, SISA by merging models trained on different shards with weight-averaging. With such modifications, we show that SISA++ preserves performance more in comparison to SISA after unlearning in benchmark SER (CREMA-D) and DD (E-DAIC) datasets. Also, to guide future research for easier adoption of MU for PSP, we present ``cookbook recipes'' - actionable recommendations for selecting optimal feature representations and downstream architectures that can mitigate performance degradation after the unlearning process.
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