The NPU System for the 2020 Personalized Voice Trigger Challenge
원문 초록
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.
함께 살펴볼 논문
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Advances in Small-Footprint Keyword Spotting: A Comprehensive Review of Efficient Models and Algorithms
SPBA: Utilizing Speech Large Language Model for Backdoor Attacks on Speech Classification Models
AnalyticKWS: Towards Exemplar-Free Analytic Class Incremental Learning for Small-footprint Keyword Spotting
EmoAttack: Utilizing Emotional Voice Conversion for Speech Backdoor Attacks on Deep Speech Classification Models
Disentangled Training with Adversarial Examples For Robust Small-footprint Keyword Spotting
TinySV: Speaker Verification in TinyML with On-device Learning
토론
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