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This paper tests the hypothesis that distinctive feature classifiers anchoredat phonetic landmarks can be transferred cross-lingually without loss ofaccuracy. Three consonant voicing classifiers were developed: (1) manuallyselected acoustic features anchored at a phonetic landmark, (2) MFCCs (eitheraveraged across the segment or anchored at the landmark), and(3) acousticfeatures computed using a convolutional neural network (CNN). All detectors aretrained on English data (TIMIT),and tested on English, Turkish, and Spanish(performance measured using F1 and accuracy). Experiments demonstrate thatmanual features outperform all MFCC classifiers, while CNNfeatures outperformboth. MFCC-based classifiers suffer an F1reduction of 16% absolute whengeneralized from English to other languages. Manual features suffer only a 5%F1 reduction,and CNN features actually perform better in Turkish and Span-ishthan in the training language, demonstrating that features capable ofrepresenting long-term spectral dynamics (CNN and landmark-based features) areable to generalize cross-lingually with little or no loss of accuracy

Landmark-based consonant voicing detection on multilingual corpora