8th Annual Conference of the International Speech Communication Association

Antwerp, Belgium
August 27-31, 2007

Language Identification Using Several Sources of Information with a Multiple-Gaussian Classifier

R. Cordoba, L. F. D'Haro, F. Fernandez-Martinez, J. M. Montero, R. Barra

Universidad Politécnica de Madrid, Spain

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.

Full Paper

Bibliographic reference.  Cordoba, R. / D'Haro, L. F. / Fernandez-Martinez, F. / Montero, J. M. / Barra, R. (2007): "Language identification using several sources of information with a multiple-Gaussian classifier", In INTERSPEECH-2007, 2137-2140.