7 papers
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…
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…
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…
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…
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…
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…