Accent Classification with Phonetic Vowel Representation
This work addresses accent classification for speech processing applications, but it is incremental as it builds on existing methods with minor improvements.
The paper tackled accent classification by combining phonetic vowel knowledge with acoustic features in a GMM classifier, achieving a 51% classification rate on a 7-way English accent task.
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.