87 citations · 161 across the 20 of their papers we have counts for
20 papers
Disentangled Training with Adversarial Examples For Robust Small-footprint Keyword Spotting
Zhenyu Wang, Li Wan, Biqiao Zhang +5
A keyword spotting (KWS) engine that is continuously running on device is exposed to various speech signals that are usually unseen before. It is a challenging problem to build a s…
Directional Source Separation for Robust Speech Recognition on Smart Glasses
Tiantian Feng, Ju Lin, Yiteng Huang +7
Modern smart glasses leverage advanced audio sensing and machine learning technologies to offer real-time transcribing and captioning services, considerably enriching human experie…
Handling the Alignment for Wake Word Detection: A Comparison Between Alignment-Based, Alignment-Free and Hybrid Approaches
Vinicius Ribeiro, Yiteng Huang, Yuan Shangguan +3
Wake word detection exists in most intelligent homes and portable devices. It offers these devices the ability to "wake up" when summoned at a low cost of power and computing. This…
LiCo-Net: Linearized Convolution Network for Hardware-efficient Keyword Spotting
Haichuan Yang, Zhaojun Yang, Li Wan +10
This paper proposes a hardware-efficient architecture, Linearized Convolution Network (LiCo-Net) for keyword spotting. It is optimized specifically for low-power processor units li…
Multi-Task Self-Supervised Pre-Training for Music Classification
Ho-Hsiang Wu, Chieh-Chi Kao, Qingming Tang +4
Deep learning is very data hungry, and supervised learning especially requires massive labeled data to work well. Machine listening research often suffers from limited labeled data…
On Front-end Gain Invariant Modeling for Wake Word Spotting
Yixin Gao, Noah D. Stein, Chieh-Chi Kao +4
Wake word (WW) spotting is challenging in far-field due to the complexities and variations in acoustic conditions and the environmental interference in signal transmission. A suite…