Automatic Detection of “g-dropping” in American English Using Forced Alignment Jiahong Yuan 1, Mark Liberman 2 University of Pennsylvania 1
[email protected] 2
[email protected] Abstract—This study investigated the use of forced alignment Previous studies on “g-dropping” have relied on for automatic detection of “g-dropping” in American English impressionistic coding of the variable. Although it is a (e.g., walkin'). Two acoustic models were trained, one for -in' and relatively easy task for native speakers to manually classify the other for -ing. The models were added to the Penn Phonetics “g-dropping” [11], it is expensive, inconsistent and Lab Forced Aligner, and forced alignment will choose the more impractical when we move on from analysing small datasets probable pronunciation from the two alternatives. The agreement rates between the forced alignment method and native to thousands of hours of speech that are now openly available. English speakers ranged from 79% to 90%, which were In this study we investigate the use of forced alignment for comparable to the agreement rates among the native speakers automatic classification of ‘g-dropping’. We evaluate the (79% - 96%). The two variations of pronunciation not only method by comparing the results with native speakers’ manual differed in their nasal codas, but also – and even more so – in coding. Finally, we explore the acoustic difference between their vowel quality. This is shown by both the KL-divergence the two variations (-in’ and -ing) through both computing the between the two models, and that native Mandarin speakers divergence between the two models, and comparing native performed poorly on classification of “g-dropping”.