Pre-Finetuning for Few-Shot Emotional Speech Recognition

Understand

Speech models have long been known to overfit individual speakers for many classification tasks.

• This leads to poor generalization in settings where the speakers are out-of-domain or out-of-distribution, as is common in production environments.
• We view speaker adaptation as a few-shot learning problem and propose investigating transfer learning approaches inspired by recent success with pre-trained models in natural language tasks.
• We propose pre-finetuning speech models on difficult tasks to distill knowledge into few-shot downstream classification objectives.