activity
20192026
most citedBYOL-S: Learning Self-supervised Speech Representations by Bootstrapping

10 citations · 14 across the 8 of their papers we have counts for

collaborators

9 papers

cs.CL2026

OLIVE: View-Augmented Latent Prediction with Waveform Reconstruction for Speech SSL

Karl El Hajal, Mathew Magimai. -Doss

We propose Online Latent prediction with Invariant Views and rEconstruction (OLIVE), a self-supervised speech representation learning framework that jointly optimizes analysis and…

eess.AS2025

Unsupervised Rhythm and Voice Conversion to Improve ASR on Dysarthric Speech

Karl El Hajal, Enno Hermann, Sevada Hovsepyan +1

Automatic speech recognition (ASR) systems struggle with dysarthric speech due to high inter-speaker variability and slow speaking rates. To address this, we explore dysarthric-to-…

eess.AS2025

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…

eess.AS2024

kNN Retrieval for Simple and Effective Zero-Shot Multi-speaker Text-to-Speech

Karl El Hajal, Ajinkya Kulkarni, Enno Hermann +1

While recent zero-shot multi-speaker text-to-speech (TTS) models achieve impressive results, they typically rely on extensive transcribed speech datasets from numerous speakers and…

eess.AS2023★ 1 cited

Speaker Embeddings as Individuality Proxy for Voice Stress Detection

Zihan Wu, Neil Scheidwasser-Clow, Karl El Hajal +1

Since the mental states of the speaker modulate speech, stress introduced by cognitive or physical loads could be detected in the voice. The existing voice stress detection benchma…

eess.AS2022★ 2 cited

Efficient Speech Quality Assessment using Self-supervised Framewise Embeddings

Karl El Hajal, Zihan Wu, Neil Scheidwasser-Clow +2

Automatic speech quality assessment is essential for audio researchers, developers, speech and language pathologists, and system quality engineers. The current state-of-the-art sys…

cs.SD2022★ 10 cited

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…

eess.AS2022★ 1 cited

MOSRA: Joint Mean Opinion Score and Room Acoustics Speech Quality Assessment

Karl El Hajal, Milos Cernak, Pablo Mainar

The acoustic environment can degrade speech quality during communication (e.g., video call, remote presentation, outside voice recording), and its impact is often unknown. Objectiv…

cs.CL2019

Neural Arabic Question Answering

Hussein Mozannar, Karl El Hajal, Elie Maamary +1

This paper tackles the problem of open domain factual Arabic question answering (QA) using Wikipedia as our knowledge source. This constrains the answer of any question to be a spa…