Tcl and the Tk toolkit
Word sense disambiguation and information retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Metadata for integrating speech documents in a text retrieval system
ACM SIGMOD Record
Automatic content-based retrieval of broadcast news
Proceedings of the third ACM international conference on Multimedia
Tree-based state tying for high accuracy acoustic modelling
HLT '94 Proceedings of the workshop on Human Language Technology
Video mail retrieval using voice: an overview of the stage 2 system
MIRO'95 Proceedings of the Final conference on Multimedia Information Retrieval
New techniques for open-vocabulary spoken document retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
New Approaches to Spoken Document Retrieval
Information Retrieval
Mixing and Merging for Spoken Document Retrieval
ECDL '98 Proceedings of the Second European Conference on Research and Advanced Technology for Digital Libraries
Video mail retrieval using voice: an overview of the stage 2 system
MIRO'95 Proceedings of the Final conference on Multimedia Information Retrieval
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This paper outlines the Video Mail Retrieval (VMR) project at Cambridge University. The goal of the VMR project is to develop an application for the retrieval of spoken documents in multimedia systems. Speech documents pose a particular problem for retrieval since the contents are unknown. The VMR project seeks to address this problem by combining state-of-the-art speech recognition with established document retrieval technologies to provide an effective and efficient retrieval tool. Experimental results with a small spoken message collection show that retrieval precision is some what dependent on the generality of the acoustic modelling used. For talker-dependent acoustic modelling retrieval performance is around 95% of that observed when text transcriptions of the same files are used. However, even with incorporation of completely open-user talker-independent acoustic models, retrieval performance of about 75% of text can be obtained.