  This paper introduces and motivates the use of hybrid robust feature
extraction technique for spoken language identification (LID) system. The
speech recognizers use a parametric form of a signal to get the most important
distinguishable features of speech signal for recognition task. In this paper
Mel-frequency cepstral coefficients (MFCC), Perceptual linear prediction
coefficients (PLP) along with two hybrid features are used for language
Identification. Two hybrid features, Bark Frequency Cepstral Coefficients
(BFCC) and Revised Perceptual Linear Prediction Coefficients (RPLP) were
obtained from combination of MFCC and PLP. Two different classifiers, Vector
Quantization (VQ) with Dynamic Time Warping (DTW) and Gaussian Mixture Model
(GMM) were used for classification. The experiment shows better identification
rate using hybrid feature extraction techniques compared to conventional
feature extraction methods.BFCC has shown better performance than MFCC with
both classifiers. RPLP along with GMM has shown best identification performance
among all feature extraction techniques.
