Language modeling for broadcast news transcription

This paper addresses the problem of language modeling for the transcription of broadcast news data. Different approaches for language model training were explored and tested in the context of a complete transcription system. Language model efficiency was investigated for the following aspects: mixing of different training material (sources and epoch); approach for mixing (interpolation vs count merging); and using class-based language models. The experimental results indicate that judicious selection of the training source and epoch is important, and that given sufficient broadcast new transcriptions, newspaper and newswire texts are not necessary. Results are given in terms of perplexity and word error rates. The combined improvements in text selection, interpolation, 4-gram and class-based LMs led to a 20% reduction in the perplexity of the LM of the final pass (3-gram class interpolated with a word 4-gram) compared with the 3-gram LM used in the the LIMSI Nov’97 BN system.