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 modulationfiltered spectrogram, 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.
This paper describes a spoken document retrieval system, combining the ABBOT large vocabulary continuous speech recognition (LVCSR) system developed by Cambridge University, Sheffield University and Softsound, and the PRISE information retrieval engine developed by NIST. The system was constructed to enable us to participate in the TREC 6 Spoken Document Retrieval experimental evaluation. Our key aims in this work were to produce a complete system for the SDR task, to investigate the effect of a word error rate of 30-50% on retrieval performance and to investigate the integration of LVCSR and word spotting in a retrieval task.
Hybrid connectionist-hidden Markov model large vocabulary speech recognition has, in recent years, been shown to be competitive with more traditional HMM systems [4]. Connectionist acoustic models generally use considerably less parameters than HMM's, allowing real-time operation without significant degradation of performance. However, the small number of parameters in connectionist acoustic models also poses a problem -how do we make the best use of large amounts of training data? This paper proposes a solution to this problem in which a "smart" procedure makes selective use of training data to increase performance.
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