Meta-PerSER uses MAML-style meta-training with combined-set training, derivative annealing, and per-layer learning rates to personalize speech emotion recognition to unseen annotators from 32 labeled examples, outperforming fine-tuning and multi-task baselines on IEMOCAP.
Proposed Meta-PerSER Table 1 demonstrates that Meta-PerSER consistently outper- forms all baseline methods across both Seen and Unseen Data scenarios and across all upstream models
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Meta-PerSER: Few-Shot Listener Personalized Speech Emotion Recognition via Meta-learning
Meta-PerSER uses MAML-style meta-training with combined-set training, derivative annealing, and per-layer learning rates to personalize speech emotion recognition to unseen annotators from 32 labeled examples, outperforming fine-tuning and multi-task baselines on IEMOCAP.