Name
Title | ||
|---|---|---|
Injecting Text and Cross-Lingual Supervision in Few-Shot Learning from Self-Supervised Models. |
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ICASSP43922.2022.9746852 | IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthew Wiesner | 1 | 2 | 1.39 |
| Desh Raj | 2 | 0 | 0.34 |
| Sanjeev Khudanpur | 3 | 2155 | 202.00 |