Connectionist speech recognition of Broadcast News

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

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Pages (from-to)27-45
Number of pages19
JournalSpeech Communication
Volume37
Issue number1–2
DOIs
Publication statusPublished - May 2002

Keywords

  • Speech recognition
  • Neural networks
  • Acoustic features
  • Pronunciation modelling
  • Search techniques
  • Stack decoder

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