10 citations · 20 across the 7 of their papers we have counts for
5 papers · 1 filter
Incorporating Talker Identity Aids With Improving Speech Recognition in Adversarial Environments
Sagarika Alavilli, Annesya Banerjee, Gasser Elbanna +1
Current state-of-the-art speech recognition models are trained to map acoustic signals into sub-lexical units. While these models demonstrate superior performance, they remain vuln…
Explaining Deep Learning Embeddings for Speech Emotion Recognition by Predicting Interpretable Acoustic Features
Satvik Dixit, Daniel M. Low, Gasser Elbanna +2
Pre-trained deep learning embeddings have consistently shown superior performance over handcrafted acoustic features in speech emotion recognition (SER). However, unlike acoustic f…
Predicting Heart Activity from Speech using Data-driven and Knowledge-based features
Gasser Elbanna, Zohreh Mostaani, Mathew Magimai. -Doss
Accurately predicting heart activity and other biological signals is crucial for diagnosis and monitoring. Given that speech is an outcome of multiple physiological systems, a sign…
BYOL-S: Learning Self-supervised Speech Representations by Bootstrapping
Gasser Elbanna, Neil Scheidwasser-Clow, Mikolaj Kegler +3
Methods for extracting audio and speech features have been studied since pioneering work on spectrum analysis decades ago. Recent efforts are guided by the ambition to develop gene…
Hybrid Handcrafted and Learnable Audio Representation for Analysis of Speech Under Cognitive and Physical Load
Gasser Elbanna, Alice Biryukov, Neil Scheidwasser-Clow +5
As a neurophysiological response to threat or adverse conditions, stress can affect cognition, emotion and behaviour with potentially detrimental effects on health in the case of s…