SISA++ uses weight averaging of shard-trained models to outperform SISA in retaining SER and DD performance after user-data unlearning on CREMA-D and E-DAIC.
Towards Machine Unlearning for Paralinguistic Speech Processing
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abstract
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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Towards Machine Unlearning for Paralinguistic Speech Processing
SISA++ uses weight averaging of shard-trained models to outperform SISA in retaining SER and DD performance after user-data unlearning on CREMA-D and E-DAIC.