255 citations · 780 across the 58 of their papers we have counts for
7 papers · 2 filters
Leveraging Domain Features for Detecting Adversarial Attacks Against Deep Speech Recognition in Noise
Christian Heider Nielsen, Zheng-Hua Tan
In recent years, significant progress has been made in deep model-based automatic speech recognition (ASR), leading to its widespread deployment in the real world. At the same time…
Filterbank Learning for Noise-Robust Small-Footprint Keyword Spotting
Iván López-Espejo, Ram C. M. C. Shekar, Zheng-Hua Tan +2
In the context of keyword spotting (KWS), the replacement of handcrafted speech features by learnable features has not yielded superior KWS performance. In this study, we demonstra…
Improved disentangled speech representations using contrastive learning in factorized hierarchical variational autoencoder
Yuying Xie, Thomas Arildsen, Zheng-Hua Tan
Leveraging the fact that speaker identity and content vary on different time scales, \acrlong{fhvae} (\acrshort{fhvae}) uses different latent variables to symbolize these two attri…
Minimum Processing Near-end Listening Enhancement
Andreas Jonas Fuglsig, Jesper Jensen, Zheng-Hua Tan +3
The intelligibility and quality of speech from a mobile phone or public announcement system are often affected by background noise in the listening environment. By pre-processing t…
Adversarial Multi-Task Deep Learning for Noise-Robust Voice Activity Detection with Low Algorithmic Delay
Claus Meyer Larsen, Peter Koch, Zheng-Hua Tan
Voice Activity Detection (VAD) is an important pre-processing step in a wide variety of speech processing systems. VAD should in a practical application be able to detect speech in…
Disentangled Speech Representation Learning Based on Factorized Hierarchical Variational Autoencoder with Self-Supervised Objective
Yuying Xie, Thomas Arildsen, Zheng-Hua Tan
Disentangled representation learning aims to extract explanatory features or factors and retain salient information. Factorized hierarchical variational autoencoder (FHVAE) present…
Complex Recurrent Variational Autoencoder with Application to Speech Enhancement
Yuying Xie, Thomas Arildsen, Zheng-Hua Tan
As an extension of variational autoencoder (VAE), complex VAE uses complex Gaussian distributions to model latent variables and data. This work proposes a complex recurrent VAE fra…