Language Identification using Warping and the Shifted Delta Cepstrum
Felicity R. Allen, Eliathamby Ambikairajah, Julien Epps · 2005
This paper proposes the novel use of feature warping for automatic language identification, in combination with the shifted delta cepstrum (SDC) and perceptual linear predictive coefficients in a Gaussian mixture model (GMM) based system. Experimental results on various configurations of front-end techniques reported herein demonstrate that, besides providing robustness against channel mismatch and noise as found in existing literature, feature warping is useful more generally as a technique for pre-mapping data for improved compatibility with a GMM back-end. The configuration reported in this paper provides a language identification performance of 76.4% using the OGI/NIST database, a 46.5% relative reduction in error rate when compared with a benchmark system employing Mel frequency cepstral coefficients and the SDC