Language recognition using language factors

Language recognition systems based on acoustic models reach state of the art performance using discriminative training techniques. In speaker recognition, eigenvoice modeling of the speaker, and the use of speaker factors as input features to SVMs has recently been demonstrated to give good results compared to the standard GMM-SVM approach, which combines GMMs supervectors and SVMs. In this paper we propose, in analogy to the eigenvoice modeling approach, to estimate an eigen-language space, and to use the language factors as input features to SVM classifiers. Since language factors are low-dimension vectors, training and evaluating SVMs with different kernels and with large training examples becomes an easy task. This approach is demonstrated on the 14 languages of the NIST 2007 language recognition task, and shows performance improvements with respect to the standard GMM-SVM technique.