Warped Magnitude and Phase-Based Features for Language Identification

Abstract

To date, systems for the identification of spoken languages have normally used magnitude-based parameterization methods such as the MFCC and PLP. This paper investigates the use of the recently proposed modified group delay function (MODGDF) coefficients in combination with traditional magnitude-based features in a Gaussian mixture model (GMM) based system. We also examine the application of feature warping to magnitude-based features and the MODGDF and find that it can offer a significant cumulative improvement. We find that the addition of a modified regression-based shifted delta cepstrum (SDC) further improves system performance beyond that obtained by a more standard SDC configuration. The combination of PLP, feature warping and the proposed regression-based SDC achieved an accuracy of 88.4% in tests on 10 languages in the OGI TS Corpus, which compares very favourably with alternative language identification systems reported in the literature