PADA: Pruning Assisted Domain Adaptation for Self-Supervised Speech Representations

• URL: http://arxiv.org/abs/2203.16965v4
• Date: Sat, 13 May 2023 21:18:45 GMT
• Title: PADA: Pruning Assisted Domain Adaptation for Self-Supervised Speech Representations
• Authors: Lodagala V S V Durga Prasad and Sreyan Ghosh and S. Umesh
• Abstract summary: We propose PADA (Pruning Assisted Domain Adaptation) and zero out redundant weights from models pre-trained on large amounts of out-of-domain (OOD) data. The redundant weights can be identified through various pruning strategies which have been discussed in detail as a part of this work. Specifically, we investigate the effect of the recently discovered Task-Agnostic and Task-Aware pruning on PADA and propose a new pruning paradigm based on the latter. Our proposed CD-TAW methodology achieves up to 20.6% relative WER improvement over our baseline when fine-tuned on a 2-hour subset of Switch
• Score: 1.2031796234206138
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While self-supervised speech representation learning (SSL) models serve a variety of downstream tasks, these models have been observed to overfit to the domain from which the unlabelled data originates. To alleviate this issue, we propose PADA (Pruning Assisted Domain Adaptation) and zero out redundant weights from models pre-trained on large amounts of out-of-domain (OOD) data. Intuitively, this helps to make space for the target-domain ASR finetuning. The redundant weights can be identified through various pruning strategies which have been discussed in detail as a part of this work. Specifically, we investigate the effect of the recently discovered Task-Agnostic and Task-Aware pruning on PADA and propose a new pruning paradigm based on the latter, which we call Cross-Domain Task-Aware Pruning (CD-TAW). CD-TAW obtains the initial pruning mask from a well fine-tuned OOD model, which makes it starkly different from the rest of the pruning strategies discussed in the paper. Our proposed CD-TAW methodology achieves up to 20.6% relative WER improvement over our baseline when fine-tuned on a 2-hour subset of Switchboard data without language model (LM) decoding. Furthermore, we conduct a detailed analysis to highlight the key design choices of our proposed method.

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