Language model transformation applied to lightly supervised training of acoustic model for congress meetings

For effective training of acoustic and language models for spontaneous speech such as meetings, it is significant to exploit the texts available in a large scale, which may not be faithful transcripts of the utterances. We have proposed a language model transformation scheme to cope with the differences between verbatim transcripts of spontaneous utterances and human-made transcripts such as those in proceedings. In this paper, we investigate its application to lightly supervised training of the acoustic model. By transforming the corresponding text in the proceedings, we can generate a very constrained model to predict the actual utterances. The experimental evaluation with the transcription system for the Japanese Congress meetings demonstrated that the proposed scheme can generate accurate labels for acoustic model training and thus realizes the comparable ASR (Automatic Speech Recognition) performance to the case using manual transcripts.