Connectionist Speech Recognition of Broadcast News

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

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.


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Speech Communication

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Academic Units
Electrical Engineering
Published Here
February 15, 2012