In this paper, multidialectal acoustic modeling based on shar-
ing data across dialects is addressed. A comparative study of
different methods of combining data based on decision tree
clustering algorithms is presented. Approaches evolved differ
in the way of evaluating the similarity of sounds between di-
alects, and the decision tree structure applied. Proposed systems
are tested with Spanish dialects across Spain and Latin Amer-
ica. All multidialectal proposed systems improve monodialectal
performance using data from another dialect but it is shown that
the way to share data is critical. The best combination between
similarity measure and tree structure achieves an improvement
of 7% over the results obtained with monodialectal systems.Peer ReviewedPostprint (published version
