10th Annual Conference of the International Speech Communication Association

Brighton, United Kingdom
September 6-10, 2009

Improving the Recognition of Names by Document-Level Clustering

Bin Zhang, Wei Wu, Jeremy G. Kahn, Mari Ostendorf

University of Washington, USA

Named entities are of great importance in spoken document processing, but speech recognizers often get them wrong because they are infrequent. A name correction method based on documentlevel name clustering is proposed in this paper, consisting of three components: named entity detection, name clustering, and name hypothesis selection. We compare the performance of this method to oracle conditions and show that the oracle gain is a 23% reduction in name character error for Mandarin and the automatic approach achieves about 20% of that.

Full Paper

Bibliographic reference.  Zhang, Bin / Wu, Wei / Kahn, Jeremy G. / Ostendorf, Mari (2009): "Improving the recognition of names by document-level clustering", In INTERSPEECH-2009, 1035-1038.