Multilingual bottleneck features for subword modelingin zero-resource languages
Table 2: Average precision scores on the same-different task (dev sets), showing the effects of applying vtln to the input features for the utd and/or cae systems. cae input is either MFCC or MFCC+VTLN. Topline results (rows 5-6) train cAE on gold standard pairs, rather than UTD output. Baseline results (final rows) directly evaluate acoustic features without UTD/cAE training. Best unsupervised result in bold.
['[BOLD] UTD input', '[BOLD] cAE input', '[BOLD] ES', '[BOLD] HA', '[BOLD] HR', '[BOLD] SV', '[BOLD] TR', '[BOLD] ZH']
We find that \glscae features as trained previously are slightly better than MFCC+VTLN, but can be improved considerably by applying \glsvtln to the input of both \glsutd and \glscae training—indeed, even using gold pairs as \glscae input applying VTLN is beneficial. This suggests that \glscae training and VTLN abstract over different aspects of the speech signal, and that both should be used when only target language data is available.