  This paper tests the hypothesis that distinctive feature classifiers anchored
at phonetic landmarks can be transferred cross-lingually without loss of
accuracy. Three consonant voicing classifiers were developed: (1) manually
selected acoustic features anchored at a phonetic landmark, (2) MFCCs (either
averaged across the segment or anchored at the landmark), and(3) acoustic
features computed using a convolutional neural network (CNN). All detectors are
trained on English data (TIMIT),and tested on English, Turkish, and Spanish
(performance measured using F1 and accuracy). Experiments demonstrate that
manual features outperform all MFCC classifiers, while CNNfeatures outperform
both. MFCC-based classifiers suffer an F1reduction of 16% absolute when
generalized from English to other languages. Manual features suffer only a 5%
F1 reduction,and CNN features actually perform better in Turkish and Span-ish
than in the training language, demonstrating that features capable of
representing long-term spectral dynamics (CNN and landmark-based features) are
able to generalize cross-lingually with little or no loss of accuracy
