Building and Evaluation of a Real Room Impulse Response Dataset
| Autor: | Jakub Paliesek, Igor Szöke, Miroslav Skácel, Jan Cernocky, Ladislav Mosner |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Reverberation
Noise measurement Microphone Computer science business.industry Speech recognition 020206 networking & telecommunications 02 engineering and technology Speaker recognition Software Audio and Speech Processing (eess.AS) Signal Processing FOS: Electrical engineering electronic engineering information engineering 0202 electrical engineering electronic engineering information engineering NIST Loudspeaker Electrical and Electronic Engineering business Impulse response Electrical Engineering and Systems Science - Audio and Speech Processing |
| Popis: | 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. Submitted to Journal of Selected Topics in Signal Processing, November 2018 |
| Databáze: | OpenAIRE |
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