activity
20172021
most citedauDeep: Unsupervised Learning of Representations from Audio with Deep Recurrent Neural Networks

19 citations · 20 across the 2 of their papers we have counts for

collaborators

7 papers

cs.HC2021

Remote smartphone-based speech collection: acceptance and barriers in individuals with major depressive disorder

Judith Dineley, Grace Lavelle, Daniel Leightley +22

The ease of in-the-wild speech recording using smartphones has sparked considerable interest in the combined application of speech, remote measurement technology (RMT) and advanced…

cs.HC2019

The Ambiguous World of Emotion Representation

Vidhyasaharan Sethu, Emily Mower Provost, Julien Epps +3

Artificial intelligence and machine learning systems have demonstrated huge improvements and human-level parity in a range of activities, including speech recognition, face recogni…

cs.HC2019

AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition

Fabien Ringeval, Björn Schuller, Michel Valstar +14

The Audio/Visual Emotion Challenge and Workshop (AVEC 2019) "State-of-Mind, Detecting Depression with AI, and Cross-cultural Affect Recognition" is the ninth competition event aime…

cs.CL20191 cited

Voice command generation using Progressive Wavegans

Thomas Wiest, Nicholas Cummins, Alice Baird +3

Generative Adversarial Networks (GANs) have become exceedingly popular in a wide range of data-driven research fields, due in part to their success in image generation. Their abili…

cs.CL2018

Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives

Jing Han, Zixing Zhang, Nicholas Cummins +1

Over the past few years, adversarial training has become an extremely active research topic and has been successfully applied to various Artificial Intelligence (AI) domains. As a…

stat.ML2018

Calibrated Prediction Intervals for Neural Network Regressors

Gil Keren, Nicholas Cummins, Björn Schuller

Ongoing developments in neural network models are continually advancing the state of the art in terms of system accuracy. However, the predicted labels should not be regarded as th…