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Landmark-based consonant voicing detection on multilingual corpora
2017
Journal of the Acoustical Society of America
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
doi:10.1121/1.4987203
fatcat:mxqoowa55bfzlf65qsblei5ala