Injecting Text and Cross-lingual Supervision in Few-shot Learning from Self-Supervised Models

• URL: http://arxiv.org/abs/2110.04863v1
• Date: Sun, 10 Oct 2021 17:33:44 GMT
• Title: Injecting Text and Cross-lingual Supervision in Few-shot Learning from Self-Supervised Models
• Authors: Matthew Wiesner, Desh Raj, Sanjeev Khudanpur
• Abstract summary: We show how universal phoneset acoustic models can leverage cross-lingual supervision to improve transfer of self-supervised representations to new languages. We also show how target-language text can be used to enable and improve fine-tuning with the lattice-free maximum mutual information objective.
• Score: 33.66135770490531
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Self-supervised model pre-training has recently garnered significant interest, but relatively few efforts have explored using additional resources in fine-tuning these models. We demonstrate how universal phoneset acoustic models can leverage cross-lingual supervision to improve transfer of pretrained self-supervised representations to new languages. We also show how target-language text can be used to enable and improve fine-tuning with the lattice-free maximum mutual information (LF-MMI) objective. In three low-resource languages these techniques greatly improved few-shot learning performance.

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