Improving broadcast news transcription by lightly supervised discriminative training

Abstract

We present our experiments on lightly supervised discriminative training with large amounts of broadcast news data for which only closed caption transcriptions are available (TDT data). In particular, we use language models biased to the closed-caption transcripts to recognise the audio data, and the recognised transcripts are then used as the training transcriptions for acoustic model training. A range of experiments that use maximum likelihood (ML) training as well as discriminative training based on either maximum mutual information (MMI) or minimum phone error (MPE) are presented. In a 5xRT broadcast news transcription system that includes adaptation, it is shown that reductions in word error rate (WER) in the range of 1% absolute can be achieved. Finally, some experiments on training data selection are presented to compare different methods of "filtering" the transcripts.