6 citations · 7 across the 3 of their papers we have counts for
6 papers
Universal Semantic Disentangled Privacy-preserving Speech Representation Learning
Biel Tura Vecino, Subhadeep Maji, Aravind Varier +11
The use of audio recordings of human speech to train LLMs poses privacy concerns due to these models' potential to generate outputs that closely resemble artifacts in the training…
Analysis and Utilization of Entrainment on Acoustic and Emotion Features in User-agent Dialogue
Daxin Tan, Nikos Kargas, David McHardy +6
Entrainment is the phenomenon by which an interlocutor adapts their speaking style to align with their partner in conversations. It has been found in different dimensions as acoust…
Contrastive Unsupervised Learning for Speech Emotion Recognition
Mao Li, Bo Yang, Joshua Levy +6
Speech emotion recognition (SER) is a key technology to enable more natural human-machine communication. However, SER has long suffered from a lack of public large-scale labeled da…
Detecting Sound-Absorbing Materials in a Room from a Single Impulse Response using a CRNN
Constantinos Papayiannis, Christine Evers, Patrick A. Naylor
The materials of surfaces in a room play an important room in shaping the auditory experience within them. Different materials absorb energy at different levels. The level of absor…
Data Augmentation of Room Classifiers using Generative Adversarial Networks
Constantinos Papayiannis, Christine Evers, Patrick A. Naylor
The classification of acoustic environments allows for machines to better understand the auditory world around them. The use of deep learning in order to teach machines to discrimi…
End-to-End Classification of Reverberant Rooms using DNNs
Constantinos Papayiannis, Christine Evers, Patrick A. Naylor
Reverberation is present in our workplaces, our homes, concert halls and theatres. This paper investigates how deep learning can use the effect of reverberation on speech to classi…