  This paper describes the system developed by the NPU team for the 2020
personalized voice trigger challenge. Our submitted system consists of two
independently trained subsystems: a small footprint keyword spotting (KWS)
system and a speaker verification (SV) system. For the KWS system, a
multi-scale dilated temporal convolutional (MDTC) network is proposed to detect
wake-up word (WuW). For SV system, Write something here. The KWS predicts
posterior probabilities of whether an audio utterance contains WuW and
estimates the location of WuW at the same time. When the posterior probability
ofWuW reaches a predefined threshold, the identity information of triggered
segment is determined by the SV system. On evaluation dataset, our submitted
system obtains detection costs of 0.081and 0.091 in close talking and far-field
tasks, respectively.
