Abstract

In this paper, we describe the BBN Byblos system used for the 1996 Hub-4 Partitioned Evaluation (PE) and Unpartitioned Evaluation (UE) tests. For the PE, we chose to ignore the segment feature labels that were given to the system as side-information so that our approach would generalize trivially to the UE. Moreover, we chose not to model specific channel conditions in the training because the observed gains were too small to warrant the additional system complexity required to support them. In the end, we estimated a single set of acoustic models from only 40 hours of broadcast news data. For the UE, the data was automatically segmented with a simple dual-gender phoneme recognizer that efficiently located pauses and changes in speakers' gender. After this preliminary stage of segmentation and gender-classification, our UE and PE systems were identical. We achieved a 30.2% word error rate on the PE test and 31.8% on the UE test - only a 5% relative degradation from our PE result. 1. I...

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