Implicitly supervised language model adaptation for meeting transcription

Authors:
David Huggins-Daines;Alexander I. Rudnicky
Affiliations:
Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA
Venue:
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Year:
2007

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Abstract

We describe the use of meeting metadata, acquired using a computerized meeting organization and note-taking system, to improve automatic transcription of meetings. By applying a two-step language model adaptation process based on notes and agenda items, we were able to reduce perplexity by 9% and word error rate by 4% relative on a set of ten meetings recorded in-house. This approach can be used to leverage other types of metadata.