Study of probabilistic and Bottle-Neck features in multilingual environment

Abstract

This study is focused on the performance of Probabilistic and Bottle-Neck features on different language than they were trained for. It is shown, that such porting is possible and that the features are still competitive to PLP features. Further, several combination techniques are evaluated. The performance of combined features is close to the best performing system. Finally, bigger NNs were trained on large data from different domain. The resulting features outperformed previously trained systems and combination with them further improved the system performance.