  Performance degradation of an Automatic Speech Recognition (ASR) system is
commonly observed when the test acoustic condition is different from training.
Hence, it is essential to make ASR systems robust against various environmental
distortions, such as background noises and reverberations. In a multi-stream
paradigm, improving robustness takes account of handling a variety of unseen
single-stream conditions and inter-stream dynamics. Previously, a practical
two-stage training strategy was proposed within multi-stream end-to-end ASR,
where Stage-2 formulates the multi-stream model with features from Stage-1
Universal Feature Extractor (UFE). In this paper, as an extension, we introduce
a two-stage augmentation scheme focusing on mismatch scenarios: Stage-1
Augmentation aims to address single-stream input varieties with data
augmentation techniques; Stage-2 Time Masking applies temporal masks on UFE
features of randomly selected streams to simulate diverse stream combinations.
During inference, we also present adaptive Connectionist Temporal
Classification (CTC) fusion with the help of hierarchical attention mechanisms.
Experiments have been conducted on two datasets, DIRHA and AMI, as a
multi-stream scenario. Compared with the previous training strategy,
substantial improvements are reported with relative word error rate reductions
of 29.7-59.3% across several unseen stream combinations.
