Online environmental adaptation of CNN-based acoustic models using spatial diffuseness features

We propose a new concept for adapting CNN-based acoustic models using spatial diffuseness features as auxiliary information about the acoustic environment: the spatial diffuseness features are simultaneously employed as acoustic-model input features and to estimate environmental cues for context adaptation, where one convolutional layer is factorized into several sub-layers to represent different acoustic conditions. This context-adaptive CNN-based acoustic model facilitates an online environmental adaptation and is experimentally verified for the real-world recordings provided by the CHiME-3 task. The best performing setup reduces the average word error rate scores achieved by the baseline system (without using spatial diffuseness features) from 19.4% to 15.9% and 12.2% to 10.7% considering two experimental setups with and without front-end signal enhancement, respectively.