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Improving the Dependency on Fine-Tuning Data in Transfer Learning for Speech Emotion Recognition
Compensating for train-test mismatches in acoustic environments is important for deployable speech solutions. This paper proposes to reduce the amount of fine-tuning data needed in transfer learning to mitigate the train-test mismatch by using a compensation mechanism based on the feature maximum li
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