Το work with title Gaussian mixture clustering and language adaptation for the development of a new language speech recognition system by Chatzichrisafis, Nikos, Diakoloukas Vasilis, Digalakis Vasilis, Harizakis Costas is licensed under Creative Commons Attribution 4.0 International
Bibliographic Citation
N. Chatzichrisafis, V. Diakoloukas, V. Digalakis and C. Harizakis, "Gaussian mixture clustering and language adaptation for the development of a new language speech recognition system," IEEE Trans. Audio, Speech, Language Process., vol. 15, no. 3, pp. 928-938, Mar. 2007. doi:10.1109/TASL.2006.885259
https://doi.org/10.1109/TASL.2006.885259
The 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 available