DBN based multi-stream models for speech

Abstract

We propose dynamic Bayesian network (DBN) based synchronous and asynchronous multi-stream models for noise-robust automatic speech recognition. In these models, multiple noise-robust features are combined into a single DBN to obtain better performance than any single feature system alone. Results on the Aurora 2.0 noisy speech task show significant improvements of our synchronous model over both single stream models and over a ROVER based fusion method.