255 citations · 780 across the 58 of their papers we have counts for
12 papers · 1 filter
PAC-Bayesian-Like Error Bound for a Class of Linear Time-Invariant Stochastic State-Space Models
Deividas Eringis, John Leth, Zheng-Hua Tan +2
In this paper we derive a PAC-Bayesian-Like error bound for a class of stochastic dynamical systems with inputs, namely, for linear time-invariant stochastic state-space models (st…
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…
Improving Label-Deficient Keyword Spotting Through Self-Supervised Pretraining
Holger Severin Bovbjerg, Zheng-Hua Tan
Keyword Spotting (KWS) models are becoming increasingly integrated into various systems, e.g. voice assistants. To achieve satisfactory performance, these models typically rely on…
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…
Floor Map Reconstruction Through Radio Sensing and Learning By a Large Intelligent Surface
Cristian J. Vaca-Rubio, Roberto Pereira, Xavier Mestre +4
Environmental scene reconstruction is of great interest for autonomous robotic applications, since an accurate representation of the environment is necessary to ensure safe interac…
User Localization using RF Sensing: A Performance comparison between LIS and mmWave Radars
Cristian J. Vaca-Rubio, Dariush Salami, Petar Popovski +3
Since electromagnetic signals are omnipresent, Radio Frequency (RF)-sensing has the potential to become a universal sensing mechanism with applications in localization, smart-home,…
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…
On Training Targets and Activation Functions for Deep Representation Learning in Text-Dependent Speaker Verification
Achintya kr. Sarkar, Zheng-Hua Tan
Deep representation learning has gained significant momentum in advancing text-dependent speaker verification (TD-SV) systems. When designing deep neural networks (DNN) for extract…