Rapid acoustic model development using Gaussian mixture clustering and language adaptation

Abstract

This work presents techniques for improved cross-language transfer of speech recognition systems to new, previously undeveloped, 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 hours of read speech is available.