Automatic alignment for New Englishes: Applying state-of-the-art aligners to Trinidadian English

While forced alignment has become an essential part of data processing in phonetic research, state-of-the-art aligners are often exclusively tailor-made for majority dialects, such as American English(es). This paper provides the first in-depth investigation into the reliability of popular pre-trained aligners in New Englishes-the nativized, postcolonial Englishes spoken world-wide. Using manually aligned data from Trinidadian English, the paper examines popular aligners [Forced Alignment and Vowel Extraction (FAVE), Munich Automatic Segmentation (MAUS), and the Montreal Forced Aligner (MFA)] and their performances in automatically segmenting Trinidadian speech. Results show that, first, only specific aligners (FAVE and MFA) can provide alignment that is comparable to that in the training varieties and, to a smaller degree, general human inter-rater uncertainty. Second, even well-performing aligners introduce bias toward their training varieties: the aligners systematically produce more erroneous alignments of Trinidadian English-specific vowels, for which they have no acoustic models. The findings suggest that phonetic research on New Englishes can benefit from pre-trained, state-of-the-art aligners, but that further manual data processing may generally be required to minimize errors in the analysis of non-majority dialect data.