Integration of acoustic information and PPRLM scores in a multiple-Gaussian classifier for Language Identification

In this paper, we present several innovative techniques that can be applied in a PPRLM system for language identification (LID). We will show how we obtained a 53.5% relative error reduction from our base system using several techniques. First, the application of a variable threshold in score computation, dependent on the average scores in the language model, provided a 35% error reduction. A random selection of sentences for the different sets and the use of silence models also improved the system. Then, to improve the classifier, we compared the bias removal technique (up to 19% error reduction) and a Gaussian classifier (up to 37% error reduction). Finally, we included the acoustic score in the Gaussian classifier (2% error reduction) and increased the number of Gaussians to have a multiple-Gaussian classifier (14% error reduction). We will show how all these improvements are remarkable as they have been mostly additive