18 citations · 117 across the 21 of their papers we have counts for
7 papers · 1 filter
The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates
Björn W. Schuller, Anton Batliner, Christian Bergler +21
The INTERSPEECH 2021 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the CO…
High-Fidelity Audio Generation and Representation Learning with Guided Adversarial Autoencoder
Kazi Nazmul Haque, Rajib Rana, Björn W Schuller
Unsupervised disentangled representation learning from the unlabelled audio data, and high fidelity audio generation have become two linchpins in the machine learning research fiel…
A Novel Fusion of Attention and Sequence to Sequence Autoencoders to Predict Sleepiness From Speech
Shahin Amiriparian, Pawel Winokurow, Vincent Karas +3
Motivated by the attention mechanism of the human visual system and recent developments in the field of machine translation, we introduce our attention-based and recurrent sequence…
On Deep Speech Packet Loss Concealment: A Mini-Survey
Mostafa M. Mohamed, Mina A. Nessiem, Björn W. Schuller
Packet-loss is a common problem in data transmission, using Voice over IP. The problem is an old problem, and there has been a variety of classical approaches that were developed t…
ConcealNet: An End-to-end Neural Network for Packet Loss Concealment in Deep Speech Emotion Recognition
Mostafa M. Mohamed, Björn W. Schuller
Packet loss is a common problem in data transmission, including speech data transmission. This may affect a wide range of applications that stream audio data, like streaming applic…
"I have vxxx bxx connexxxn!": Facing Packet Loss in Deep Speech Emotion Recognition
Mostafa M. Mohamed, Björn W. Schuller
In applications that use emotion recognition via speech, frame-loss can be a severe issue given manifold applications, where the audio stream loses some data frames, for a variety…