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Connectionist Speech Recognition of Broadcast News

A. J. Robinson; G. D. Cook; Daniel P. W. Ellis; Eric Fosler-Lussier; S. J. Renals; D. A. G. Williams

Title:
Connectionist Speech Recognition of Broadcast News
Author(s):
Robinson, A. J.
Cook, G. D.
Ellis, Daniel P. W.
Fosler-Lussier, Eric
Renals, S. J.
Williams, D. A. G.
Date:
Type:
Articles
Department:
Electrical Engineering
Volume:
37
Permanent URL:
Book/Journal Title:
Speech Communication
Abstract:
This paper describes connectionist techniques for recognition of Broadcast News. The fundamental difference between connectionist systems and more conventional mixture-of-Gaussian systems is that connectionist models directly estimate posterior probabilities as opposed to likelihoods. Access to posterior probabilities has enabled us to develop a number of novel approaches to confidence estimation, pronunciation modelling and search. In addition we have investigated a new feature extraction technique based on the modulation-filtered spectrogram (MSG), and methods for combining multiple information sources. We have incorporated all of these techniques into a system for the transcription of Broadcast News, and we present results on the 1998 DARPA Hub-4E Broadcast News evaluation data.
Subject(s):
Artificial intelligence
Communication
Publisher DOI:
http://dx.doi.org/10.1016/S0167-6393(01)00058-9
Item views:
71
Metadata:
text | xml

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