On the selection of the impulse responses for distant-speech recognition based on contaminated speech training

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Light Gated Recurrent Units for Speech Recognition (2018, background): “More details on the realistic impulse responses adopted in this corpus can be found in [56]–[58].”
The DIRHA-ENGLISH corpus and related tasks for distant-speech recognition in domestic environments (2015, background): “Our past experience [30, 33, 14] indeed suggests that, even though the use of language models is certainly helpful in increasing the recognition performance, the adoption of a simple phone-loop task is more suitable for experiments purely focusing on the acoustic information.”
Batch-normalized joint training for DNN-based distant speech recognition (2016, background): “Nevertheless, the recent success of deep learning has not only largely contributed to the substantial improvement of the speech recognition part of a DSR system [19, 20, 21, 22, 23, 24, 25], but has also enabled the de-a r X i v : 1703 .”
Realistic Multi-Microphone Data Simulation for Distant Speech Recognition (2016, background): “Approaches as contaminated speech training [14, 15, 16], multi-style training [17, 18, 19] and data augmentation [20, 21, 22, 23] have, in fact, been shown very effective in improving the DSR system performance.”
Contaminated speech training methods for robust DNN-HMM distant speech recognition (2017, methodology): “This approach has also been adopted recently in [3, 4, 23, 24].”