Automatic Transcription of Multi-Genre Media Archives
Pierre Lanchantin, Peter Bell, Mark Gales, Thomas Hain, Xunying Liu, Yanhua Long, Jennifer Quinnell, Steve J. Renals, Óscar Saz, M.S. Seigel, Paweł Świętojański, Philip C. Woodland · White Rose Research Online (University of Leeds, The University of Sheffield, University of York) · 2013
This paper describes some recent results of our collaborative work on developing a speech recognition system for the automatic transcription or media archives from the British Broadcasting Corporation (BBC). Material includes a high diversity of shows with their associated transcriptions. The latter are highly diverse in terms of completeness, reliability and accuracy. First, we investigate how to improve lightly supervised acoustic training when time-stamps information is inaccurate or when speech deviates significantly from the transcription. To address the last issue, word and segment level combination approaches are used between the lightly supervised transcripts and the original programme scripts which yield improved transcriptions. Experimental results show that systems trained using these improved transcriptions consistently outperform those trained using only the original lightly supervised decoding hypotheses. Secondly, we show that the recognition task may benefit from systems trained on a combination of in-domain and out-of-domain data. Working with tandem HMMs, we present Multi-level Adaptive Networks, a novel technique for incorporating information from out-of domain posterior features using deep neural network. We show that it provides a substantial reduction in WER over other systems including PLP baseline, in-domain tandem features and best out-of-domain tandem features.