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

Matthew Wiesner; Desh Raj; Sanjeev Khudanpur

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arXiv · 2110.04863

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

Matthew Wiesner·Desh Raj·Sanjeev Khudanpur

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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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Matthew Wiesner, Desh Raj, Sanjeev Khudanpur. 2021-10-10. Injecting Text and Cross-lingual Supervision in Few-shot Learning from Self-Supervised Models. https://arxiv.org/abs/2110.04863

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