Gaussian mixture clustering and language adaptation for the development of a new language speech recognition systemGaussian mixture clustering and language adaptation for the development of a new language speech recognition system Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2015-11-022007enThe porting of a speech recognition system to a new language is usually a time-consuming and expensive process since it requires collecting, transcribing, and processing a large amount of language-specific training sentences. This work presents techniques for improved cross-language transfer of speech recognition systems to new target languages. Such techniques are particularly useful for target languages where minimal amounts of training data are available. We describe a novel method to produce a language-independent system by combining acoustic models from a number of source languages. This intermediate language-independent acoustic model is used to bootstrap a target-language system by applying language adaptation. For our experiments, we use acoustic models of seven source languages to develop a target Greek acoustic model. We show that our technique significantly outperforms a system trained from scratch when less than 8 h of read speech is availablehttp://creativecommons.org/licenses/by/4.0/IEEE Transactions on Audio, Speech and Language Processing153928-938 Chatzichrisafis, Nikos Diakoloukas Vasilis Διακολουκας Βασιλeioς Digalakis Vasilis Διγαλακης Βασιλης Harizakis Costas Institute of Electrical and Electronics Engineers Natural languages Speech recognition Training data Linear discriminant analysis Speech processing Databases Costs Application software Acoustical engineering Vectors