Improved acoustic word embeddings for zero-resource languages using multilingual transfer
TABLE VIII: Accuracy (%) of linear classifiers predicting the language identity of triphones from the development data of five languages (three seen, two unseen).
['Model', 'RU', 'RU+CS', 'RU+CS+FR', 'Multilingual']
Adding more training languages generally improves language classification performance, but this effect is stronger for the ClassifierRNN and SiameseRNN than for the CAE-RNN (which actually performs best when only trained on three languages). An analysis of the individual F1-scores (not shown here) per language shows there are no consistent differences between seen (RU, CS, FR) and unseen (DE, ZH) languages. While triphones from the same language seem to cluster together, there is a lot of overlap across languages, potentially enabling the model to generalise the acquired phonetic properties of training languages to unseen languages. Note, however, that such generalisation may not work equally well for all languages: in the figure there seems to be more overlap between the training languages (RU, CS, FR) and DE (another Indo-European language) than between the training languages and ZH (a Sino-Tibetan language).