  This paper introduces the contents and the possible usage of the
DIRHA-ENGLISH multi-microphone corpus, recently realized under the EC DIRHA
project. The reference scenario is a domestic environment equipped with a large
number of microphones and microphone arrays distributed in space.
  The corpus is composed of both real and simulated material, and it includes
12 US and 12 UK English native speakers. Each speaker uttered different sets of
phonetically-rich sentences, newspaper articles, conversational speech,
keywords, and commands. From this material, a large set of 1-minute sequences
was generated, which also includes typical domestic background noise as well as
inter/intra-room reverberation effects. Dev and test sets were derived, which
represent a very precious material for different studies on multi-microphone
speech processing and distant-speech recognition. Various tasks and
corresponding Kaldi recipes have already been developed.
  The paper reports a first set of baseline results obtained using different
techniques, including Deep Neural Networks (DNN), aligned with the
state-of-the-art at international level.
