학술논문
Building and evaluation of a real room impulse response dataset
Document Type
Periodical
Author
Source
IEEE Journal of Selected Topics in Signal Processing IEEE J. Sel. Top. Signal Process. Selected Topics in Signal Processing, IEEE Journal of. 13(4):863-876 Aug, 2019
Subject
Language
ISSN
1932-4553
1941-0484
1941-0484
Abstract
This paper presents BUT ReverbDB—a dataset of real room impulse responses (RIR), background noises, and retransmitted speech data. The retransmitted data include 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 eight 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.