11th Annual Conference of the International Speech Communication Association

Makuhari, Chiba, Japan
September 26-30. 2010

Direct Construction of Compact Context-Dependency Transducers from Data

David Rybach (1), Michael Riley (2)

(1) RWTH Aachen University, Germany
(2) Google, USA

This paper describes a new method for building compact context-dependency transducers for finite-state transducer-based ASR decoders. Instead of the conventional phonetic decision-tree growing followed by FST compilation, this approach incorporates the phonetic context splitting directly into the transducer construction. The objective function of the split optimization is augmented with a regularization term that measures the number of transducer states introduced by a split. We give results on a large spoken-query task for various n-phone orders and other phonetic features that show this method can greatly reduce the size of the resulting context-dependency transducer with no significant impact on recognition accuracy. This permits using context sizes and features that might otherwise be unmanageable.

Full Paper

Bibliographic reference.  Rybach, David / Riley, Michael (2010): "Direct construction of compact context-dependency transducers from data", In INTERSPEECH-2010, 218-221.