Automatic phonological transcription using forced alignment : FAVE toolkit performance on four non-standard varieties of English

Abstract: Forced alignment, a speech recognition software performing semi-automatic phonological transcription, constitutes a methodological revolution in the recent history of linguistic research. Its use is progressively becoming the norm in research fields such as sociophonetics, but its general performance and range of applications have been relatively understudied. This thesis investigates the performance and portability of the Forced Alignment and Vowel Extraction program suite (FAVE), an aligner that was trained on, and designed to study, American English. It was decided to test FAVE on four non-American varieties of English (Scottish, Irish, Australian and Indian English) and a control variety (General American). First, the performance of FAVE was compared with human annotators, and then it was tested on three potentially problematic variables: /p, t, k/ realization, rhotic consonants and /l/. Although FAVE was found to perform significantly differently from human annotators on identical datasets, further analysis revealed that the aligner performed quite similarly on the non-standard varieties and the control variety, suggesting that the difference in accuracy does not constitute a major drawback to its extended usage. The study discusses the implications of the findings in relation to doubts expressed about the usage of such technology and argues for a wider implementation of forced alignment tools such as FAVE in sociophonetic research.