September 22-25, 1997
In this paper, we describe our recent work in automatic transcription of broadcast news programming from ra- dio and television. This is a very challenging recogni- tion problem because of the frequent and unpredictable changes that occur in speaker, speaking style, topic, chan- nel, and background conditions. Faced with such a prob- lem, there is a strong tendency to try to carve the in- put into separable classes and deal with each one inde- pendently. We have chosen instead to rely on condition- independent models and adaptive algorithms to deal with this highly variable data. In addition, we have developed effective techniques to automatically segment the input waveform and cluster the segments into data sets contain- ing similar speakers and conditions to support unsuper- vised adaptation on the test. Using this general approach, we achieved the best overall word error rate of 31.8% on the 1996 DARPA Hub-4 Unpartitioned Evaluation.
Bibliographic reference. Kubala, Francis / Jin, Hubert / Matsoukas, Spyros / Nguyen, Long / Schwartz, Richard / Makhoul, John (1997): "Advances in transcription of broadcast news", In EUROSPEECH-1997, 927-930.