INJECTING TEXT AND CROSS-LINGUAL SUPERVISION IN FEW-SHOT LEARNING FROM SELF-SUPERVISED MODELS
Matthew Wiesner, Desh Raj, Sanjeev Khudanpur
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SPS
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Self-supervised model pretraining has recently garnered significant interest. However, using additional resources in fine-tuning these models has received less attention. 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.