Improved Accent Classification Combining Phonetic Vowels with Acoustic Features

TL;DR

This paper presents an improved accent classification system that combines phonetic vowel knowledge with enhanced acoustic features, achieving competitive accuracy with short speech segments.

Contribution

It introduces a novel integration of phonetic vowels with optimized acoustic features using PCA and HLDA in a GMM-UBM framework for accent classification.

Findings

Achieved 54% accuracy on FAE corpus with 20-second test segments.

Combining phonetic vowels with acoustic features improves classification performance.

System is competitive with state-of-the-art accent classification methods.

Abstract

Researches have shown accent classification can be improved by integrating semantic information into pure acoustic approach. In this work, we combine phonetic knowledge, such as vowels, with enhanced acoustic features to build an improved accent classification system. The classifier is based on Gaussian Mixture Model-Universal Background Model (GMM-UBM), with normalized Perceptual Linear Predictive (PLP) features. The features are further optimized by Principle Component Analysis (PCA) and Hetroscedastic Linear Discriminant Analysis (HLDA). Using 7 major types of accented speech from the Foreign Accented English (FAE) corpus, the system achieves classification accuracy 54% with input test data as short as 20 seconds, which is competitive to the state of the art in this field.