Multidialectal Acoustic Modeling: a Comparative Study

In this paper, multidialectal acoustic modeling based on sharing 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 dialects, and the decision tree structure applied. Proposed systems are tested with Spanish dialects across Spain and Latin America. 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.