In this paper we present our latest investigation on multilingual bottle-neck (BN) features and its application to rapid language adaptation to new languages. We show that the overall performance of a Multilayer Perceptron (MLP) network improves significantly by initializing it with a multilingual MLP. Furthermore, ASR performance increases on both, on those languages which were used for multilingual MLP training, and on a new language. We propose a new strategy called "open target language"

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... to train more flexible models for language adaptation, which is particularly suited for small amounts of training data. The final results on the Vietnamese GlobalPhone database gave 15.8% relative improvement in terms of Syllable Error Rate (SyllER) for the ASR system trained with 22.5h data and 16.9% relative gains for the system trained with only 2h data.