1 citations · 2 across the 5 of their papers we have counts for
5 papers
Comparing Self-Supervised Learning Models Pre-Trained on Human Speech and Animal Vocalizations for Bioacoustics Processing
Eklavya Sarkar, Mathew Magimai. -Doss
Self-supervised learning (SSL) foundation models have emerged as powerful, domain-agnostic, general-purpose feature extractors applicable to a wide range of tasks. Such models pre-…
Unsupervised Rhythm and Voice Conversion of Dysarthric to Healthy Speech for ASR
Karl El Hajal, Enno Hermann, Ajinkya Kulkarni +1
Automatic speech recognition (ASR) systems are well known to perform poorly on dysarthric speech. Previous works have addressed this by speaking rate modification to reduce the mis…
Feature Representations for Automatic Meerkat Vocalization Classification
Imen Ben Mahmoud, Eklavya Sarkar, Marta Manser +1
Understanding evolution of vocal communication in social animals is an important research problem. In that context, beyond humans, there is an interest in analyzing vocalizations o…
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…
Can Self-Supervised Neural Representations Pre-Trained on Human Speech distinguish Animal Callers?
Eklavya Sarkar, Mathew Magimai. -Doss
Self-supervised learning (SSL) models use only the intrinsic structure of a given signal, independent of its acoustic domain, to extract essential information from the input to an…