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 8-28% relative compared to the current production system.

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In the 2008 presidential election race in the United States, the prospective candidates made extensive use of YouTube to post video material. We developed a scalable system that transcribes this material and makes the content searchable (by indexing the meta-data and transcripts of the videos) and allows the user to navigate through the video material based on content. The system is available as an iGoogle gadget 1 as well as a Labs product (labs.google.com/gaudi). Given the large exposure, special emphasis was put on the scalability and reliability of the system. This paper describes the design and implementation of this system.