  One of the most challenging scenarios for smart speakers is multi-talker,
when target speech from the desired speaker is mixed with interfering speech
from one or more speakers. A smart assistant needs to determine which voice to
recognize and which to ignore and it needs to do so in a streaming, low-latency
manner. This work presents two multi-microphone speech enhancement algorithms
targeted at this scenario. Targeting on-device use-cases, we assume that the
algorithm has access to the signal before the hotword, which is referred to as
the noise context. First is the Context Aware Beamformer which uses the noise
context and detected hotword to determine how to target the desired speaker.
The second is an adaptive noise cancellation algorithm called Speech Cleaner
which trains a filter using the noise context. It is demonstrated that the two
algorithms are complementary in the signal-to-noise ratio conditions under
which they work well. We also propose an algorithm to select which one to use
based on estimated SNR. When using 3 microphone channels, the final system
achieves a relative word error rate reduction of 55% at -12dB, and 43\% at
12dB.
