activity
20202026
most citedRediscovering the Slavic Continuum in Representations Emerging from Neural Models of Spoken Language Identification

8 citations · 10 across the 16 of their papers we have counts for

collaborators

16 papers

cs.CL2026

From Monolingual to Multilingual: Evaluating Mamba for ASR in South African Languages

Jesujoba O. Alabi, Julian Herreilers, Badr M. Abdullah +1

Recent advances in automatic speech recognition (ASR) have explored different sequence models, including Conformer-based models and newer state space models such as Mamba. Although…

cs.CL2026

Ethio-ASR: Joint Multilingual Speech Recognition and Language Identification for Ethiopian Languages

Badr M. Abdullah, Israel Abebe Azime, Atnafu Lambebo Tonja +14

We present Ethio-ASR, a suite of multilingual CTC-based automatic speech recognition (ASR) models jointly trained on five Ethiopian languages: Amharic, Tigrinya, Oromo, Sidaama, an…

cs.CL2025

It's Not a Walk in the Park! Challenges of Idiom Translation in Speech-to-text Systems

Iuliia Zaitova, Badr M. Abdullah, Wei Xue +3

Idioms are defined as a group of words with a figurative meaning not deducible from their individual components. Although modern machine translation systems have made remarkable pr…

cs.CL2025

Voice Conversion Improves Cross-Domain Robustness for Spoken Arabic Dialect Identification

Badr M. Abdullah, Matthew Baas, Bernd Möbius +1

Arabic dialect identification (ADI) systems are essential for large-scale data collection pipelines that enable the development of inclusive speech technologies for Arabic language…

cs.CL2025

Attention on Multiword Expressions: A Multilingual Study of BERT-based Models with Regard to Idiomaticity and Microsyntax

Iuliia Zaitova, Vitalii Hirak, Badr M. Abdullah +3

This study analyzes the attention patterns of fine-tuned encoder-only models based on the BERT architecture (BERT-based models) towards two distinct types of Multiword Expressions…

cs.CL2024★ 2 cited

On the Encoding of Gender in Transformer-based ASR Representations

Aravind Krishnan, Badr M. Abdullah, Dietrich Klakow

While existing literature relies on performance differences to uncover gender biases in ASR models, a deeper analysis is essential to understand how gender is encoded and utilized…

cs.CL2023

Information-Theoretic Characterization of Vowel Harmony: A Cross-Linguistic Study on Word Lists

Julius Steuer, Badr Abdullah, Johann-Mattis List +1

We present a cross-linguistic study that aims to quantify vowel harmony using data-driven computational modeling. Concretely, we define an information-theoretic measure of harmonic…

cs.CL2023

An Information-Theoretic Analysis of Self-supervised Discrete Representations of Speech

Badr M. Abdullah, Mohammed Maqsood Shaik, Bernd Möbius +1

Self-supervised representation learning for speech often involves a quantization step that transforms the acoustic input into discrete units. However, it remains unclear how to cha…

cs.CL2023

Analyzing the Representational Geometry of Acoustic Word Embeddings

Badr M. Abdullah, Dietrich Klakow

Acoustic word embeddings (AWEs) are vector representations such that different acoustic exemplars of the same word are projected nearby in the embedding space. In addition to their…

cs.CL2022

Integrating Form and Meaning: A Multi-Task Learning Model for Acoustic Word Embeddings

Badr M. Abdullah, Bernd Möbius, Dietrich Klakow

Models of acoustic word embeddings (AWEs) learn to map variable-length spoken word segments onto fixed-dimensionality vector representations such that different acoustic exemplars…

cs.CL2021

How Familiar Does That Sound? Cross-Lingual Representational Similarity Analysis of Acoustic Word Embeddings

Badr M. Abdullah, Iuliia Zaitova, Tania Avgustinova +2

How do neural networks "perceive" speech sounds from unknown languages? Does the typological similarity between the model's training language (L1) and an unknown language (L2) have…

cs.CL2021

Do Acoustic Word Embeddings Capture Phonological Similarity? An Empirical Study

Badr M. Abdullah, Marius Mosbach, Iuliia Zaitova +2

Several variants of deep neural networks have been successfully employed for building parametric models that project variable-duration spoken word segments onto fixed-size vector r…

cs.CL2021

SIGTYP 2021 Shared Task: Robust Spoken Language Identification

Elizabeth Salesky, Badr M. Abdullah, Sabrina J. Mielke +6

While language identification is a fundamental speech and language processing task, for many languages and language families it remains a challenging task. For many low-resource an…

cs.CL2020

A Closer Look at Linguistic Knowledge in Masked Language Models: The Case of Relative Clauses in American English

Marius Mosbach, Stefania Degaetano-Ortlieb, Marie-Pauline Krielke +2

Transformer-based language models achieve high performance on various tasks, but we still lack understanding of the kind of linguistic knowledge they learn and rely on. We evaluate…

cs.CL2020★ 8 cited

Rediscovering the Slavic Continuum in Representations Emerging from Neural Models of Spoken Language Identification

Badr M. Abdullah, Jacek Kudera, Tania Avgustinova +2

Deep neural networks have been employed for various spoken language recognition tasks, including tasks that are multilingual by definition such as spoken language identification. I…

eess.AS2020

Cross-Domain Adaptation of Spoken Language Identification for Related Languages: The Curious Case of Slavic Languages

Badr M. Abdullah, Tania Avgustinova, Bernd Möbius +1

State-of-the-art spoken language identification (LID) systems, which are based on end-to-end deep neural networks, have shown remarkable success not only in discriminating between…