Online Temporal Language Model Adaptation for a Thai Broadcast News Transcription System

This paper investigates the effectiveness of online temporal language model adaptation when applied to a Thai broadcast news transcription task. Our adaptation scheme works as follow: first an initial language model is trained with broadcast news transcription available during the development period. Then the language model is adapted over time with more recent broadcast news transcription and online news articles available during deployment especially the data from the same time period as the broadcast news speech being recognized. We found that the data that are closer in time are more similar in terms of perplexity and are more suitable for language model adaptation. The LMs that are adapted over time with more recent news data are better, both in terms of perplexity and WER, than the static LM trained from only the initial set of broadcast news data. Adaptation data from broadcast news transcription improved perplexity by 38.3% and WER by 7.1% relatively. Though, online news artic...

• Type: Conference
• Year: 2010
• Where: LREC
• Authors: Kwanchiva Saykham, Ananlada Chotimongkol, Chai Wutiwiwatchai
• Keywords: Education, Language Model, Language Model Adaptation, LREC 2010, Perplexity