  Accented speech recognition and accent classification are relatively
under-explored research areas in speech technology. Recently, deep
learning-based methods and Transformer-based pretrained models have achieved
superb performances in both areas. However, most accent classification tasks
focused on classifying different kinds of English accents and little attention
was paid to geographically-proximate accent classification, especially under a
low-resource setting where forensic speech science tasks usually encounter. In
this paper, we explored three main accent modelling methods combined with two
different classifiers based on 105 speaker recordings retrieved from five urban
varieties in Northern England. Although speech representations generated from
pretrained models generally have better performances in downstream
classification, traditional methods like Mel Frequency Cepstral Coefficients
(MFCCs) and formant measurements are equipped with specific strengths. These
results suggest that in forensic phonetics scenario where data are relatively
scarce, a simple modelling method and classifier could be competitive with
state-of-the-art pretrained speech models as feature extractors, which could
enhance a sooner estimation for the accent information in practices. Besides,
our findings also cross-validated a new methodology in quantifying
sociophonetic changes.
