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Recently, there is a research trend on ad-hoc microphone arrays. However,most research was conducted on simulated data. Although some data sets werecollected with a small number of distributed devices, they were notsynchronized which hinders the fundamental theoretical research to ad-hocmicrophone arrays. To address this issue, this paper presents a synchronizedspeech corpus, named Libri-adhoc40, which collects the replayed Librispeechdata from loudspeakers by ad-hoc microphone arrays of 40 strongly synchronizeddistributed nodes in a real office environment. Besides, to provide theevaluation target for speech frontend processing and other applications, wealso recorded the replayed speech in an anechoic chamber. We trained severalmulti-device speech recognition systems on both the Libri-adhoc40 dataset and asimulated dataset. Experimental results demonstrate the validness of theproposed corpus which can be used as a benchmark to reflect the trend anddifference of the models with different ad-hoc microphone arrays. The datasetis online available at https://github.com/ISmallFish/Libri-adhoc40.

Libri-adhoc40: A dataset collected from synchronized ad-hoc microphone arrays