This paper presents the comparison of different acoustic adaptation methods in a multilingual speech recognition environment. Baseline multilingual acoustic models were generated using the tree based clustering with common phonetic broad classes. After the expert based port to a new language was performed, the influence of several adaptation methods on speech recognition performance was investigated. The target language adaptation subset contained 2% of complete speech database. The best adapted ported system had significant improvement in the speech recognition performance and its results were close to the results of pure reference monolingual system. The relationship between languages used in the mapping configuration remained unchanged after the adaptation.
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