Automatic context window composition for distant speech recognition

This paper investigates on the mechanisms occurring inside DNNs, which lead to an effective application of asymmetric contexts. In particular, we propose a novel method for automatic context window composition based on a gradient analysis. The experiments, performed with different acoustic environments, features, DNN architectures, microphone settings, and recognition tasks show that our simple and efficient strategy leads to a less redundant frame configuration, which makes DNN training more effective in reverberant scenarios.