Improved acoustic word embeddings for zero-resource languages using multilingual transfer
TABLE III: AP (%) on development data when adding language conditioning (LC) in the decoder of the multilingual CAE-RNN.
['Multilingual model', 'ES', 'HA', 'HR', 'SV', 'TR', 'ZH']
To see if this leads to better generalisation to unseen languages, we compare model variants with and without language conditioning. In the ClassifierRNN, we found that split prediction branches give slightly worse performance than using a single shared softmax layer. (This improvement was also obtained on the German development data.) In the tables below we also denote the CAE-RNN with language conditioning as CAE-RNN-LC.