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Affrication as the cause of /s/-retraction : Evidence from Manchester English
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Affrication as the cause of /s/-retraction:Evidence from Manchester English
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Insertion and deletion in Northern English (ng) : Interacting innovations in the life cycle of phonological processes
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The graphical representation of phonological dialect features of the North of England on social media
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Ki(ng) in the north: Effects of duration, boundary, and pause on post-nasal [ɡ]-presence
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In: Laboratory Phonology: Journal of the Association for Laboratory Phonology; Vol 10, No 1 (2019); 3 ; 1868-6354 (2019)
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Ki(ng) in the North : effects of duration, boundary and pause on post-nasal [ɡ]-presence
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Emerging from below the social radar : Incipient evaluation in the North West of England
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Automatic detection of sociolinguistic variation using forced alignment
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Automatic Detection of Sociolinguistic Variation Using Forced Alignment
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In: University of Pennsylvania Working Papers in Linguistics (2016)
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Abstract:
Forced alignment software is now widely used in contemporary sociolinguistics, and is quickly becoming a crucial methodological tool as an increasing number of studies begin to utilise ‘big data.’ This study investigates the possibility of taking forced alignment one step further towards the goal of complete automation; specifically, it expands the functionality of FAVE-align to fully automate the coding of three sociolinguistic variables in British English: (th)-fronting, (td)-deletion, and (h)-dropping. This involved the expansion of pronouncing dictionaries to reflect the surface output of these variable rules; FAVE then compares the fit of competing acoustic models with the speech signal to determine the surface variant. It does so with an impressive degree of accuracy, largely comparable to inter-transcriber agreement for all variables; however, the pattern of its mistakes, which are largely false positives, suggests a difficulty in identifying the voiceless segments of (td) and (th). Although it is reassuring that inter-transcriber agreement was also lowest for these tokens, it should be noted that FAVE’s accuracy decreases in faster speech rates while no comparable effect is found for agreement among human transcribers.
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URL: https://repository.upenn.edu/pwpl/vol22/iss2/3 https://repository.upenn.edu/cgi/viewcontent.cgi?article=1923&context=pwpl
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