Language identification using several sources of information with a multiple-Gaussian classifier

We present several innovative techniques that can be applied in a PPRLM system for language identification (LID). To normalize the scores, eliminate the bias in the scores and improve the classifier, we compared the bias removal technique (up to 19% relative improvement (RI)) and a Gaussian classifier (up to 37% RI). Then, we include additional sources of information in different feature vectors of the Gaussian classifier: the sentence acoustic score (11% RI), the average acoustic score for each phoneme (11% RI), and the average duration for each phoneme (7.8% RI). The use of a multiple-Gaussian classifier with 4 feature vectors meant an additional 15.1% RI. Using 4 feature vectors instead of just PPRLM provides a 26.1% RI. Finally, we include additional acoustic HMMs of the same language with success (10% relative improvement). We will show how all these improvements have been mostly additive.