Abstract:
Recently, there is a research trend on ad-hoc mi-crophone arrays. However, most research was conducted on simulated data. Although some datasets were collected with a sma...Show MoreMetadata
Abstract:
Recently, there is a research trend on ad-hoc mi-crophone arrays. However, most research was conducted on simulated data. Although some datasets were collected with a small number of distributed devices, they were not synchronized which hinders the fundamental theoretical research on ad-hoc microphone arrays. To address this issue, this paper presents a synchronized speech corpus, named Libri-adhoc40, which collects the replayed Librispeech data from loudspeakers by ad-hoc microphone arrays of 40 strongly synchronized distributed nodes in a real office environment. Besides, to provide the evaluation target for speech frontend processing and other applications, we also recorded the replayed speech in an anechoic chamber. We trained several multi-device speech recognition systems on both the Libri-adhoc40 dataset and a simulated dataset. Experimental results demonstrate the validity of the proposed corpus which can be used as a benchmark to reflect the trend and difference of the models with different ad-hoc microphone arrays. The dataset is online available at https://github.com/ISmallFish/Libri-adhoc40.
Published in: 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)
Date of Conference: 14-17 December 2021
Date Added to IEEE Xplore: 03 February 2022
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Conference Location: Tokyo, Japan