Progress in transcription of Broadcast News using Byblos ?
In this paper, we describe our progress during the last four years (19951999) in automatic transcription of broadcast news from radio and television using the BBN Byblos speech recognition system. Overall, we achieved steady progress as reflected through the results of the last four DARPA Hub-4 evaluations, with word error rates of 42.7%, 31.8%, 20.4% and 14.7% in 1995, 1996, 1997 and 1998, respectively. This progress can be attributed to improvements in acoustic modeling, channel and speaker adaptation, and search algorithms, as well as dealing with specific characteristics of the real-life variable speech found in broadcast news. Besides improving recognition accuracy, we also succeeded in developing several algorithms to achieve close-to-real-time recognition speed without a significant sacrifice in recognition accuracy.
speech recognition acoustic modeling adaptation search algorithms broadcast news transcription hidden markov models single-tree fast-match fast gaussian computation grammar spreading