Crosslingual acoustic model development for automatic speech recognition

In this work we discuss the development of two crosslingual acoustic model sets for automatic speech recognition (ASR). The starting point is a set of multilingual Spanish-English-German hidden Markov models (HMMs). The target languages are Slovenian and French. During the discussion the problem of defining a multilingual phoneme set and the associated dictionary mapping is considered. A method is described to circumvent related problems. The impact of the acoustic source models on the performance of the target systems is analyzed in detail. Several crosslingual defined target systems are built and compared to their monolingual counterparts. It is shown that crosslingual build acoustic models clearly outperform pure monolingual models if only a limited amount of target data is available.