Recent advances in the IBM GALE Mandarin transcription system

Abstract

This paper describes the system and algorithmic developments in the automatic transcription of Mandarin broadcast speech made at IBM in the second year of the DARPA GALE program. Technical advances over our previous system include improved acoustic models using embedded tone modeling, and a new topic-adaptive language model (LM) rescoring technique based on dynamically generated LMs. We present results on three community-defined test sets designed to cover both the broadcast news and the broadcast conversation domain. It is shown that our new baseline system attains a 15.4% relative reduction in character error rate compared with our previous GALE evaluation system. And a further 13.6% improvement over the baseline is achieved with the two described techniques.