Comparative Analysis of Task-Specific and Task-Agnostic Self-Supervised Pre-Training for Low-Resource ASR on LibriSpeech

Self-supervised pre-training could effectively improve the performance of low-resource automatic speech recognition (ASR). However, existing self-supervised pre-training are task-agnostic, i.e., could be applied to various downstream tasks. Although it enlarges the scope of its application, the capacity of the pre-trained model is not fully utilized for the ASR task, and the learned representations may not be optimal for ASR. In this work, in order to build a better pre-trained model for low-resource ASR, we propose a pre-training approach called wav2vec-S, where we use task-specific semi-supe

Research goal: How does the performance of task-specific self-supervised pre-training for low-resource ASR compare to task-agnostic models when evaluated on the LibriSpeech benchmark with varying amounts of fine-tuning data?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.6/10.