NDLI: Advances in speech transcription at IBM under the DARPA EARS program

Content Provider	IEEE Xplore Digital Library
Author	Chen, S.F. Kingsbury, B. Lidia Mangu Povey, D. Saon, G. Soltau, H. Zweig, G.
Copyright Year	2006
Abstract	This paper describes the technical and system building advances made in IBM's speech recognition technology over the course of the Defense Advanced Research Projects Agency (DARPA) Effective Affordable Reusable Speech-to-Text (EARS) program. At a technical level, these advances include the development of a new form of feature-based minimum phone error training (fMPE), the use of large-scale discriminatively trained full-covariance Gaussian models, the use of septaphone acoustic context in static decoding graphs, and improvements in basic decoding algorithms. At a system building level, the advances include a system architecture based on cross-adaptation and the incorporation of 2100 h of training data in every system component. We present results on English conversational telephony test data from the 2003 and 2004 NIST evaluations. The combination of technical advances and an order of magnitude more training data in 2004 reduced the error rate on the 2003 test set by approximately 21% relative-from 20.4% to 16.1%-over the most accurate system in the 2003 evaluation and produced the most accurate results on the 2004 test sets in every speed category
Sponsorship	IEEE Signal Processing Society
Starting Page	1596
Ending Page	1608
Page Count	13
File Size	1000204
File Format	PDF
ISSN	15587916
Volume Number	14
Issue Number	5
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-09-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Ear Decoding Training data System testing Speech recognition Large-scale systems Context modeling Buildings Telephony Acoustic testing Viterbi decoding Discriminative training Effective Affordable Reusable Speech-to-Text (EARS) finite-state transducer full covariance modeling large-vocabulary conversational speech recognition
Content Type	Text
Resource Type	Article
Subject	Acoustics and Ultrasonics Electrical and Electronic Engineering

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