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This paper presents the pilot evaluation of Spoken Term Detection technologies, held during the latter part of 2006. Spoken Term Detection systems rapidly detect the presence of a term, which is a sequence of words consecutively spoken, in a large audio corpus of heterogeneous speech material. The paper describes the evaluation task posed to Spoken Term(More)
We present the design and results of the Spring 2007 (RT-07) Rich Transcription Meeting Recognition Evaluation; the fifth in a series of community wide evaluations of language technologies in the meeting domain. For 2007, we supported three evaluation tasks: Speech-To-Text (STT) transcription, " Who Spoke When " Diarization (SPKR), and Speaker Attributed(More)
We present the design and results of the Spring 2006 (RT-06S) Rich Transcription Meeting Recognition Evaluation; the fourth in a series of community-wide evaluations of language technologies in the meeting domain. For 2006, we supported three evaluation tasks in two meeting sub-domains: the Speech-To-Text (STT) transcription task, and the " Who Spoke When "(More)
This paper presents the design and results of the Rich Transcription Spring 2005 (RT-05S) Meeting Recognition Evaluation. This evaluation is the third in a series of community-wide evaluations of language technologies in the meeting domain. For 2005, four evaluation tasks were supported. These included a speech-to-text (STT) transcription task and three(More)
  • Jonathan G Fiscus, Jerome Ajot, Nicolas Radde, Christophe Laprun
  • 2006
Since 1987, the National Institute of Standards and Technology has been providing evaluation infrastructure for the Automatic Speech Recognition (ASR), and more recently referred to as the Speech-To-Text (STT), research community. From the first efforts in the Resource Management domain to the present research, the NIST SCoring ToolKit (SCTK) has formed the(More)
We are developing a novel framework, PRIDE (PRediction In Dynamic Environments), to perform moving object prediction for unmanned ground vehicles. The underlying concept is based upon a multi-resolutional, hierarchical approach that incorporates multiple prediction algorithms into a single, unifying framework. The lower levels of the framework utilize(More)
We are developing a novel framework, PRIDE (PRediction In Dynamic Environments), to perform moving object prediction (MOP) to assist unmanned ground vehicles in performing path planning within dynamic environments. In addition to predicting the location of moving objects in the environment, we have extended PRIDE to generate simulated traffic during on-road(More)