  In this paper, we introduce a streaming keyphrase detection system that can
be easily customized to accurately detect any phrase composed of words from a
large vocabulary. The system is implemented with an end-to-end trained
automatic speech recognition (ASR) model and a text-independent speaker
verification model. To address the challenge of detecting these keyphrases
under various noisy conditions, a speaker separation model is added to the
feature frontend of the speaker verification model, and an adaptive noise
cancellation (ANC) algorithm is included to exploit cross-microphone noise
coherence. Our experiments show that the text-independent speaker verification
model largely reduces the false triggering rate of the keyphrase detection,
while the speaker separation model and adaptive noise cancellation largely
reduce false rejections.
