Accent Classification with Phonetic Vowel Representation

TL;DR

This paper introduces an accent classification method combining phonetic vowel knowledge with acoustic features, using a GMM classifier optimized by HLDA, achieving competitive accuracy on major English accents.

Contribution

It presents a novel approach integrating phonetic vowel information with acoustic features for accent classification, improving upon previous purely acoustic methods.

Findings

Achieved 51% accuracy on 7-way accent classification

Integrated phonetic vowels with GMM and HLDA for improved performance

Competitive results compared to state-of-the-art methods

Abstract

Previous accent classification research focused mainly on detecting accents with pure acoustic information without recognizing accented speech. This work combines phonetic knowledge such as vowels with acoustic information to build Guassian Mixture Model (GMM) classifier with Perceptual Linear Predictive (PLP) features, optimized by Hetroscedastic Linear Discriminant Analysis (HLDA). With input about 20-second accented speech, this system achieves classification rate of 51% on a 7-way classification system focusing on the major types of accents in English, which is competitive to the state-of-the-art results in this field.