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 Spanish-English-German HMMs to Slovenian. Test results show that substantial improvements over not adapted models can be achieved, confirming the efficiency of the method.
Cite as: Diehl, F., Moreno, A., Monte, E. (2005) Crosslingual adaptation of semi-continuous HMMS using acoustic regression classes and sub-simplex projection. Proc. Applied Spoken Language Interaction in Distributed Environments (ASIDE 2005), paper 18
@inproceedings{diehl05_aside,
author={Frank Diehl and Asunción Moreno and Enric Monte},
title={{Crosslingual adaptation of semi-continuous HMMS using acoustic regression classes and sub-simplex projection}},
year=2005,
booktitle={Proc. Applied Spoken Language Interaction in Distributed Environments (ASIDE 2005)},
pages={paper 18}
}