Improved acoustic word embeddings for zero-resource languages using multilingual transfer
TABLE II: AP (%) on Hausa development data when systematically reducing the noise from the UTD discovery procedure.
['Training set', 'CAE-RNN', 'SiameseRNN']
Using forced alignments, we compare each UTD term with the ground truth word token with which it overlaps most. We first correct end-pointing: the start and end frame positions predicted by the UTD system are changed to match that of the ground truth word with maximal overlap (row 2). We then keep the UTD end-pointing, but only train on pairs where the ground truth labels match (row 3). In row 4, we correct both the end-pointing and only train on pairs with the same true label. We see that as the UTD pairs are updated with correct labels (row 3) and additionally also end-pointing (row 4), the difference in performance between the CAE-RNN and SiameseRNN shrinks to around 9% absolute in AP.