Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - A segmentally informed solution to automatic accent classification and its advantages to forensic applications
AU - Brown, Georgina
AU - Franco-Pedroso, Javier
AU - Gonzalez-Rodriguez, Joaquin
PY - 2022/7/8
Y1 - 2022/7/8
N2 - Traditionally, work in automatic accent recognition has followed a similar research trajectory to that of language identification, dialect identification and automatic speaker recognition. The same acoustic modelling approaches that have been implemented in speaker recognition (such as GMM-UBM and i-vector-based systems) have also been applied to automatic accent recognition. These approaches form models of speakers’ accents by taking acoustic features from right across the speech signal without knowledge of its phonetic content. Particularly for accent recognition, however, phonetic information is expected to add substantial value to the task. The current work presents an alternative modelling approach to automatic accent recognition, which forms models of speakers’ pronunciation systems using segmental information. This article claims that such an approach to the problem makes for a more explainable method and therefore is a more appropriate method to deploy in settings where it is important to be able to communicate methods, such as forensic applications. We discuss the issue of explainability and show how the system operates on a large 700-speaker dataset of non-native English conversational telephone recordings.
AB - Traditionally, work in automatic accent recognition has followed a similar research trajectory to that of language identification, dialect identification and automatic speaker recognition. The same acoustic modelling approaches that have been implemented in speaker recognition (such as GMM-UBM and i-vector-based systems) have also been applied to automatic accent recognition. These approaches form models of speakers’ accents by taking acoustic features from right across the speech signal without knowledge of its phonetic content. Particularly for accent recognition, however, phonetic information is expected to add substantial value to the task. The current work presents an alternative modelling approach to automatic accent recognition, which forms models of speakers’ pronunciation systems using segmental information. This article claims that such an approach to the problem makes for a more explainable method and therefore is a more appropriate method to deploy in settings where it is important to be able to communicate methods, such as forensic applications. We discuss the issue of explainability and show how the system operates on a large 700-speaker dataset of non-native English conversational telephone recordings.
KW - automatic accent recognition
KW - explainable technologies
KW - segmental features
KW - forensic applications
U2 - 10.1558/ijsll.20446
DO - 10.1558/ijsll.20446
M3 - Journal article
VL - 28
SP - 201
EP - 232
JO - International Journal of Speech, Language and the Law
JF - International Journal of Speech, Language and the Law
SN - 1748-8885
IS - 2
ER -