| Paper: | SLP-L9.3 | ||
| Session: | Spoken Language Identification | ||
| Time: | Thursday, May 18, 17:10 - 17:30 | ||
| Presentation: | Lecture | ||
| Topic: | Speech and Spoken Language Processing: Language Identification | ||
| Title: | Warped Magnitude and Phase-Based Features for Language Identification | ||
| Authors: | Felicity Allen, Eliathamby Ambikairajah, University of New South Wales, Australia; Julien Epps, National ICT Australia, Australia | ||
| 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. | ||
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