Efficient Fine-Tuning of English Speech Models for Flemish Dutch Performance

With excellent generalization ability, self-supervised speech models have shown impressive performance on various downstream speech tasks in the pre-training and fine-tuning paradigm. However, as the growing size of pre-trained models, fine-tuning becomes practically unfeasible due to heavy computation and storage overhead, as well as the risk of overfitting. Adapters are lightweight modules inserted into pre-trained models to facilitate parameter-efficient adaptation. In this paper, we propose an effective adapter framework designed for adapting self-supervised speech models to the speaker ve

Research goal: Can efficient fine-tuning techniques like adapter modules or parameter-efficient tuning improve the downstream task performance of English pre-trained speech models on Flemish Dutch while reducing compute costs, measured by WER and training throughput on LibriSpeech?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.5/10.