Multilingual bottleneck features for subword modelingin zero-resource languages
Table 4: ap on the same-different task when training cae on the 10-lingual bnfs from above (cAE-BNF) with UTD and gold standard word pairs (test set results). Baselines are MFCC+VTLN and the cAE models from rows 4 and 6 of Table 2 that use MFCC+VTLN as input features. Best result without target language supervision in bold.
['[BOLD] Features', '[BOLD] ES', '[BOLD] HA', '[BOLD] HR', '[BOLD] SV', '[BOLD] TR', '[BOLD] ZH']
We trained the \glscae with the same sets of same-word pairs as before, but replaced VTLN-adapted MFCCs with the 10-lingual \glsbnfs as input features without any other changes in the training procedure. The limiting factor appears to be the quality of the \glsutd pairs. With gold standard pairs, the \glscae features improve in all languages.