Jerome Ajot

The rich transcription 2005 spring meeting recognition evaluation

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 diarization tasks: “Who Spoke When”, “Speech Activity Detection”, and “Source Localization.” The latter two were first-time experimental proof-of-concept tasks and were treated as “dry runs”. For the STT task, the lowest word error rate for the multiple distant microphone condition was 30.0% which represented an impressive 33% relative reduction from the best result obtained in the last such evaluation – the Rich Transcription Spring 2004 Meeting Recognition Evaluation. For the diarization “Who Spoke When” task, the lowest diarization error rate was 18.56% which represented a 19% relative reduction from that of RT-04S.

Towards autonomous on-road driving via multiresolutional and hierarchical moving-object prediction

In this paper, we present the PRIDE framework (Prediction In Dynamic Environments), which is a hierarchical multi-resolutional approach for moving object prediction that incorporates multiple prediction algorithms into a single, unifying framework. PRIDE is based upon the 4D/RCS (Real-time Control System) and provides information to planners at the level of granularity that is appropriate for their planning horizon. The lower levels of the framework utilize estimation theoretic short-term predictions based upon an extended Kalman filter that provide predictions and associated uncertainty measures. The upper levels utilize a probabilistic prediction approach based upon situation recognition with an underlying cost model that provide predictions that incorporate environmental information and constraints. These predictions are made at lower frequencies and at a level of resolution more in line with the needs of higher-level planners. PRIDE is run in the systems’ world model independently of the planner and the control system. The results of the prediction are made available to a planner to allow it to make accurate plans in dynamic environments. We have applied this approach to an on-road driving control hierarchy being developed as part of the DARPA Mobile Autonomous Robotic Systems (MARS) effort.