Recent advances in speech recognition for spontaneous speech translation

Abstract

Speech recognition research for speech translation at ATR will be reviewed. For spontaneous speech recognition, new methods for parameter extraction will be shown that yield acoustic measures to allow discriminative acoustic model training. A new HMM state-sharing algorithm (ML-SSS), fine-acoustic modeling using stochastic segment models, and recurrent neural networks will also be described. The MAP-VFS speaker adaptation techniques will be presented in the context of speaker clustered acoustic models. As for language modeling, variable order N-gram models and task adaptation of language modeling will be described. In addition, further research efforts on pronunciation modeling and training using error characteristics will also be reviewed. Finally, an outline of the continuous speech recognition system (ATR-SPREC) for spontaneous speech will be presented, along with a review of recent recognition experiment results.