255 citations · 780 across the 58 of their papers we have counts for
8 papers · 1 filter
Adversarial Example Detection by Classification for Deep Speech Recognition
Saeid Samizade, Zheng-Hua Tan, Chao Shen +1
Machine Learning systems are vulnerable to adversarial attacks and will highly likely produce incorrect outputs under these attacks. There are white-box and black-box attacks regar…
Deep Joint Embeddings of Context and Content for Recommendation
Miklas S. Kristoffersen, Jacob L. Wieland, Sven E. Shepstone +2
This paper proposes a deep learning-based method for learning joint context-content embeddings (JCCE) with a view to context-aware recommendations, and demonstrate its application…
On Loss Functions for Supervised Monaural Time-Domain Speech Enhancement
Morten Kolbæk, Zheng-Hua Tan, Søren Holdt Jensen +1
Many deep learning-based speech enhancement algorithms are designed to minimize the mean-square error (MSE) in some transform domain between a predicted and a target speech signal.…
Keyword Spotting for Hearing Assistive Devices Robust to External Speakers
Iván López-Espejo, Zheng-Hua Tan, Jesper Jensen
Keyword spotting (KWS) is experiencing an upswing due to the pervasiveness of small electronic devices that allow interaction with them via speech. Often, KWS systems are speaker-i…
rVAD: An Unsupervised Segment-Based Robust Voice Activity Detection Method
Zheng-Hua Tan, Achintya kr. Sarkar, Najim Dehak
This paper presents an unsupervised segment-based method for robust voice activity detection (rVAD). The method consists of two passes of denoising followed by a voice activity det…
Deep-Learning-Based Audio-Visual Speech Enhancement in Presence of Lombard Effect
Daniel Michelsanti, Zheng-Hua Tan, Sigurdur Sigurdsson +1
When speaking in presence of background noise, humans reflexively change their way of speaking in order to improve the intelligibility of their speech. This reflex is known as Lomb…
Time-Contrastive Learning Based Deep Bottleneck Features for Text-Dependent Speaker Verification
Achintya kr. Sarkar, Zheng-Hua Tan, Hao Tang +2
There are a number of studies about extraction of bottleneck (BN) features from deep neural networks (DNNs)trained to discriminate speakers, pass-phrases and triphone states for im…
Subjective Annotations for Vision-Based Attention Level Estimation
Andrea Coifman, Péter Rohoska, Miklas S. Kristoffersen +2
Attention level estimation systems have a high potential in many use cases, such as human-robot interaction, driver modeling and smart home systems, since being able to measure a p…