Parallel Transformation Network features for speaker recognition

The use of speaker adaptation transforms as features for speaker recognition is an appealing alternative to conventional short-term cepstral features. In general, this kind of methods are language dependent and limited by the need of speech recognition in the client speakers language. In this paper, we generalize a recently pro posed method -named Transformation Network features with SVM modeling- in order to become language independent and overcome the need for accurate speech recognition. This is accomplished by using a set of parallel acoustic models in several different languages to obtain a high-dimensional Parallel Transformation Network feature vector for speaker characterization.