Evaluation of multi-level context-dependent acoustic model for large vocabulary speaker adaptation tasks

In this paper, we investigate the ability of a recently proposed discriminatively trained, multi-level context-dependent acoustic model to adapt to a new speaker in both supervised and unsupervised adaptation scenarios. Speaker adaptive speech recognition experiments performed on a large-vocabulary spoken lecture task show that the multi-level model reduces word error rates by more than 10% in both cases as compared to the conventional clustering-based decision-tree context-dependent acoustic model approach.