ACCDIST: An Accent Similarity Metric for Accent Recognition and Diagnosis

Abstract

ACCDIST is a metric of the similarity between speakers’ accents that is largely uninfluenced by the individual characteristics of the speakers’ voices. In this article we describe the ACCDIST approach and contrast its performance with formant and spectral-envelope similarity measures. Using a database of 14 regional accents of the British Isles, we show that the ACCDIST metric outperforms linear discriminant analysis based on either spectral-envelope or normalised formant features. Using vowel measurements from 10 male and 10 female speakers in each accent, the best spectral-envelope metric assigned the correct accent group to a held-out speaker 78.8% of the time, while the best normalised formant-frequency metric was correct 89.4% of the time. The ACCDIST metric based on spectral-envelope features, scored 92.3%. ACCDIST is also effective in clustering speakers by accent and has applications in speech technology, language learning, forensic phonetics and accent studies.