  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.
