11 citations · 11 across the 2 of their papers we have counts for
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eess.AS2020
Towards Data-efficient Modeling for Wake Word Spotting
Yixin Gao, Yuriy Mishchenko, Anish Shah +2
Wake word (WW) spotting is challenging in far-field not only because of the interference in signal transmission but also the complexity in acoustic environments. Traditional WW mod…
eess.AS2019★ 11 cited
Compression of Acoustic Event Detection Models with Low-rank Matrix Factorization and Quantization Training
Bowen Shi, Ming Sun, Chieh-Chi Kao +3
In this paper, we present a compression approach based on the combination of low-rank matrix factorization and quantization training, to reduce complexity for neural network based…
eess.AS2019
Semi-supervised Acoustic Event Detection based on tri-training
Bowen Shi, Ming Sun, Chieh-Chi Kao +3
This paper presents our work of training acoustic event detection (AED) models using unlabeled dataset. Recent acoustic event detectors are based on large-scale neural networks, wh…