Low-resource Accent Classification in Geographically-proximate Settings: A Forensic and Sociophonetics Perspective

TL;DR

This study compares traditional and deep learning methods for classifying geographically-proximate accents in low-resource forensic settings, highlighting the effectiveness of simple models with limited data.

Contribution

It introduces a comparative analysis of accent modeling techniques in low-resource forensic contexts, emphasizing the viability of traditional methods alongside pretrained models.

Findings

Traditional methods like MFCCs perform competitively with pretrained models.

Simple models and classifiers are effective in low-resource accent classification.

The study validates a new approach to quantifying sociophonetic changes.

Abstract

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.