Automatic Sociophonetics:Exploring corpora using a forensic accent recognition system

Brown, Georgina and Wormald, Jessica (2017) Automatic Sociophonetics:Exploring corpora using a forensic accent recognition system. Journal of the Acoustical Society of America, 142 (1). pp. 422-433. ISSN 0001-4966

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Abstract

This paper demonstrates how the Y-ACCDIST system, the York ACCDIST-based automatic accent recognition system [Brown (2015). Proceedings of the International Congress of Phonetic Sciences, Glasgow, UK], can be used to inspect sociophonetic corpora as a preliminary “screening” tool. Although Y-ACCDIST's intended application is to assist with forensic casework, the system can also be exploited in sociophonetic research to begin unpacking variation. Using a subset of the PEBL (Panjabi-English in Bradford and Leicester) corpus, the outputs of Y-ACCDIST are explored, which, it is argued, efficiently and objectively assess speaker similarities across different linguistic varieties. The ways these outputs corroborate with a phonetic analysis of the data are also discovered. First, Y-ACCDIST is used to classify speakers from the corpus based on language background and region. A Y-ACCDIST cluster analysis is then implemented, which groups speakers in ways consistent with more localised networks, providing a means of identifying potential communities of practice. Additionally, the results of a Y-ACCDIST feature selection task that indicates which specific phonemes are most valuable in distinguishing between speaker groups are presented. How Y-ACCDIST outputs can be used to reinforce more traditional sociophonetic analyses and support qualitative interpretations of the data is demonstrated.

Item Type:
Journal Article
Journal or Publication Title:
Journal of the Acoustical Society of America
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/3100/3102
Subjects:
?? ACOUSTICS AND ULTRASONICS ??
ID Code:
124955
Deposited By:
Deposited On:
01 May 2018 09:56
Refereed?:
Yes
Published?:
Published
Last Modified:
22 Sep 2023 23:53