Experiments in automatic meeting transcription using JRTK

We describe our early exploration of automatic recognition of conversational speech in meetings for use in automatic summarizers and browsers to produce meeting minutes effectively and rapidly. To achieve optimal performance we started from two different baseline English recognizers adapted to meeting conditions and tested the resulting performance. The data were found to be highly disfluent (conversational human to human speech), noisy (due to lapel microphones and environment), and overlapped with background noise, resulting in error rates comparable so far to those on the CallHome conversational database (40-50% WER). A meeting browser is presented that allows the user to search and skim through highlights from a meeting efficiently despite the recognition errors