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End-to-end Alexa Device Arbitration

2021-12-08 16:43:13
Jarred Barber, Yifeng Fan, Tao Zhang
arXiv_SD
     
Abstract

We introduce a variant of the speaker localization problem, which we call device arbitration. In the device arbitration problem, a user utters a keyword that is detected by multiple distributed microphone arrays (smart home devices), and we want to determine which device was closest to the user. Rather than solving the full localization problem, we propose an end-to-end machine learning system. This system learns a feature embedding that is computed independently on each device. The embeddings from each device are then aggregated together to produce the final arbitration decision. We use a large-scale room simulation to generate training and evaluation data, and compare our system against a signal processing baseline.

Abstract (translated)

URL

https://arxiv.org/abs/2112.04914

PDF

https://arxiv.org/pdf/2112.04914.pdf

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