This paper describes the technical and system building advances made to the Google
Home multichannel speech recognition system, which was launched in November 2016.
Technical advances include an adaptive dereverberation frontend, the use of neural
network models that do multichannel processing jointly with acoustic modeling, and
grid lstms to model frequency variations. On the system level, improvements include
adapting the model using Google Home specific data. We present results on a variety
of multichannel sets. The combination of technical and system advances result in a
reduction of WER of over 18\% relative compared to the current production system.