Crosslingual acoustic model development for automatics speech recognition

Abstract

In this work we discuss the development of two cross-lingual 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 cross-lingual defined target systems are built and compared to their monolingual counterparts. It is shown that cross-lingual build acoustic models clearly outperform pure monolingual models if only a limited amount of target data is available.