270 citations · 287 across the 14 of their papers we have counts for
10 papers
Learning Emotional Representations from Imbalanced Speech Data for Speech Emotion Recognition and Emotional Text-to-Speech
Shijun Wang, Jón Guðnason, Damian Borth
Effective speech emotional representations play a key role in Speech Emotion Recognition (SER) and Emotional Text-To-Speech (TTS) tasks. However, emotional speech samples are more…
Sparsified Model Zoo Twins: Investigating Populations of Sparsified Neural Network Models
Dominik Honegger, Konstantin Schürholt, Damian Borth
With growing size of Neural Networks (NNs), model sparsification to reduce the computational cost and memory demand for model inference has become of vital interest for both resear…
Fine-grained Emotional Control of Text-To-Speech: Learning To Rank Inter- And Intra-Class Emotion Intensities
Shijun Wang, Jón Guðnason, Damian Borth
State-of-the-art Text-To-Speech (TTS) models are capable of producing high-quality speech. The generated speech, however, is usually neutral in emotional expression, whereas very o…
Federated and Privacy-Preserving Learning of Accounting Data in Financial Statement Audits
Marco Schreyer, Timur Sattarov, Damian Borth
The ongoing 'digital transformation' fundamentally changes audit evidence's nature, recording, and volume. Nowadays, the International Standards on Auditing (ISA) requires auditors…
Generative Data Augmentation Guided by Triplet Loss for Speech Emotion Recognition
Shijun Wang, Hamed Hemati, Jón Guðnason +1
Speech Emotion Recognition (SER) is crucial for human-computer interaction but still remains a challenging problem because of two major obstacles: data scarcity and imbalance. Many…
Hyper-Representations for Pre-Training and Transfer Learning
Konstantin Schürholt, Boris Knyazev, Xavier Giró-i-Nieto +1
Learning representations of neural network weights given a model zoo is an emerging and challenging area with many potential applications from model inspection, to neural architect…