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Personalized Keyphrase Detection using Speaker and Environment Information

Rajeev Vijay Rikhye
Qiao Liang
Ding Zhao
Yiteng (Arden) Huang
Interspeech 2021


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 the cross-microphone noise coherence. Our experiments show that the text-independent speaker recognition model largely reduces the false triggering rate of the keyphrase detection, while the speaker separation model and adaptive noise cancellation largely reduce false rejections.

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