Foreign accent classification using source generator based prosodic features
John H. L. Hansen, Levent M. Arslan · 2002
Speaker accent is an important issue in the formulation of robust speaker independent recognition systems. Knowledge gained from a reliable accent classification approach could improve overall recognition performance. In this paper, a new algorithm is proposed for foreign accent classification of American English. A series of experimental studies are considered which focus on establishing how speech production is varied to convey accent. The proposed method uses a source generator framework, recently proposed for analysis and recognition of speech under stress [5]. An accent sensitive database is established using speakers of American English with foreign language accents. An initial version of the classification algorithm classified speaker accent from among four different accents with an accuracy of 81.5% in the case of unknown text, and 88.9% assuming known text. Finally, it is shown that as ascent sensitive word count increases, the ability to correctly classify accent also increases, achieving an overall classification rate of 92% among four accent classes.