Multi-stream ASR trained with heterogeneous reverberant environments

A common problem with current automatic speech recognition (ASR) systems is that the performance degrades when it is presented with speech from a different acoustic environment than the one used during training. An important cause is that the feature distribution to which the ASR system is trained no longer matches that of a new environment. Reverberant environments can be especially harmful. In this work, we test a multi-stream system in which the constituent streams are each trained in separate acoustic environments. When training the acoustic modeling stages of the streams separately with clean data and heavily reverberated data, we find that that the combined system can improve the ASR performance with unseen reverberated test data.