Comparing the performance of forced aligners used in sociophonetic research
Date
2020
Authors
Gonzalez Ochoa, Simon
Grama, James
Travis, Catherine
Journal Title
Journal ISSN
Volume Title
Publisher
De Gruyter Mouton
Abstract
Forced aligners have revolutionized sociophonetics, but while there are several forced aligners available, there are few systematic comparisons of their performance. Here, we consider four major forced aligners used in sociophonetics today: MAUS, FAVE, LaBB-CAT and MFA. Through comparisons with human coders, we find that both aligner and phonological context affect the quality of automated alignments of vowels extracted from English sociolinguistic interview data. MFA and LaBB-CAT produce the highest quality alignments, in some cases not significantly different from human alignment, followed by FAVE, and then MAUS. Aligners are less accurate placing boundaries following a vowel than preceding it, and they vary in accuracy across manner of articulation, particularly for following boundaries. These observations allow us to make specific recommendations for manual correction of forced alignment.
Description
Keywords
forced alignment, accuracy comparison, sociophonetics, vowels, workflow optimization
Citation
Collections
Source
Linguistics Vanguard
Type
Journal article
Book Title
Entity type
Access Statement
Open Access