  Recently, there is a research trend on ad-hoc microphone arrays. However,
most research was conducted on simulated data. Although some data sets were
collected with a small number of distributed devices, they were not
synchronized which hinders the fundamental theoretical research to 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 validness 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.
