Recent advances in speech recognition system for IBM DARPA communicator

Abstract

In this paper, we present methods to improve speech recognition performance of the IBM DARPA Communicator system. Our efforts for acoustic modeling include training a domain specific yet broad acoustic model, speaker clustering and speaker adaptation using feature space transforms. For language modeling, we achieved improvements by using compound words, carefully designed LM classes and adjusting the within class probabilities, using NLU state information to enhance the language model and building a language model with embedded grammar objects. Our efforts produced a relative error rate reduction of 34.6% on the test set that consists of 1173 utterances that IBM received during the NIST evaluation of the DARPA Communicator systems in June 2000. We also tested our decoding on the data from some other sites to further demonstrate the robustness of the system improvements.