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One of the most challenging scenarios for smart speakers is multi-talker,when target speech from the desired speaker is mixed with interfering speechfrom one or more speakers. A smart assistant needs to determine which voice torecognize and which to ignore and it needs to do so in a streaming, low-latencymanner. This work presents two multi-microphone speech enhancement algorithmstargeted at this scenario. Targeting on-device use-cases, we assume that thealgorithm has access to the signal before the hotword, which is referred to asthe noise context. First is the Context Aware Beamformer which uses the noisecontext and detected hotword to determine how to target the desired speaker.The second is an adaptive noise cancellation algorithm called Speech Cleanerwhich trains a filter using the noise context. It is demonstrated that the twoalgorithms are complementary in the signal-to-noise ratio conditions underwhich they work well. We also propose an algorithm to select which one to usebased on estimated SNR. When using 3 microphone channels, the final systemachieves a relative word error rate reduction of 55% at -12dB, and 43\% at12dB.

Streaming Noise Context Aware Enhancement For Automatic Speech Recognition in Multi-Talker Environments