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Abstract for mrva_icslp06

Proc ICSLP, September 2006, Pittsburgh, PA, USA.

UNSUPERVISED LANGUAGE MODEL ADAPTATION FOR MANDARIN BROADCAST CONVERSATION TRANSCRIPTION

D. Mrva and P.C. Woodland

September 2006

This paper investigates unsupervised language model adaptation on a new task of Mandarin broadcast conversation transcription. It was found that N-gram adaptation yields 1.1% absolute character error rate gain and continuous space language model adaptation done with PLSA and LDA brings 1.3% absolute gain. Moreover, using broadcast news language model alone trained on large data under-performs a model that includes additional small amount of broadcast conversations by 1.8% absolute character error rate. Although, broadcast news and broadcast conversation tasks are related, this result shows their large mismatch. In addition, it was found that it is possible to do a reliable detection of broadcast news and broadcast conversation data with the N-gram adaptation.


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