This paper presents BUT ReverbDB - a dataset of real room impulse responses
(RIR), background noises and re-transmitted speech data. The retransmitted data
includes LibriSpeech test-clean, 2000 HUB5 English evaluation and part of 2010
NIST Speaker Recognition Evaluation datasets. We provide a detailed description
of RIR collection (hardware, software, post-processing) that can serve as a
"cook-book" for similar efforts. We also validate BUT ReverbDB in two sets of
automatic speech recognition (ASR) experiments and draw conclusions for
augmenting ASR training data with real and artificially generated RIRs. We show
that a limited number of real RIRs, carefully selected to match the target
environment, provide results comparable to a large number of artificially
generated RIRs, and that both sets can be combined to achieve the best ASR
results. The dataset is distributed for free under a non-restrictive license
and it currently contains data from 8 rooms, which is growing. The distribution
package also contains a Kaldi-based recipe for augmenting publicly available
AMI close-talk meeting data and test the results on an AMI single distant
microphone set, allowing it to reproduce our experiments.

该论文提出了一个真实空间脉冲响应、背景噪声和重传音频数据的数据集，并在两组自动语音识别实验中验证了其应用价值，结果表明精心挑选的有限数量的真实空间脉冲响应与大量人工生成的响应相比，能够取得同等优秀的 ASR 结果；同时，该论文介绍了脉冲响应数据的处理过程和音频数据增广技术，并开放了这个数据集的非限制性许可证。