Language adaptation of multilingual phone models for vocabulary independent speech recognition tasks

This paper presents our new results on multilingual phone modeling and adaptation into a new target language which is not included in the trained multilingual models. The experiments were carried out with the SpeechDat(M) and MacroPhone databases including the languages of French, German, Italian, Portuguese, Spanish and American English. First, we constructed language-dependent and multilingual phone models. The recognition rate for an isolated word task decreased in average only by 3.2% using 95 multilingual instead of 232 language-dependent models. Second, we investigated adaptation techniques for cross-language transfer and showed that only 100 utterances from a new language were needed for adaptation. Using the MAP algorithm the recognition rate was improved from 79.9% to 84.3%. Finally, we defined a phonetic based dissimilarity measure between 2 languages and compared language-dependent and multilingual models for the purpose of cross-language transfer.