SDC Feature-based Language Identification Using GMM-UBM

This paper presents an automatic language identification(LID) system which uses shifted delta cepstra(SDC) feature vectors and universal background model(UBM).SDC feature is created by stacking delta cepstra computed across multiple speech frames and is involved with much more temporal information than conventional MFCC feature.UBM represents the characteristic of all different languages and each language model is obtained by employing the Bayesian adaptation from this UBM.Compared with the conventional GMM method,the training and testing speed of this method is much faster.This system performance is evaluated on the OGI corpus.The best identification accuracy for 11-languages is 73.28% for 10-s utterances,82.62% for 30-s utterances and 85.23% for 45-s utterances.The processing speed is about 0.03 times real time.