Crosslingual Adaptation of Semi-continuous Hmms Using Acoustic Regression Classes and Sub-simplex Projection

With the demand on providing automatic speech recognition (ASR) systems for many markets the question of porting an ASR system to a new language is of practical interest. To cope with this task the adaptation of hidden Markov models (HMM) is seen as a key step to transfer the models from a source to a target language. In this work we introduce a novel adaptation scheme for semi-continuous HMMs (SCHMM) and apply it to a crosslingual model adaptation task. The task consists in transferring multilingual SpanishEnglish-German HMMs to Slovenian. Test results show that substantial improvements over not adapted models can be achieved, confirming the efficiency of the method.