3 citations · 9 across the 7 of their papers we have counts for
7 papers
Tracking the emergence of linguistic structure in self-supervised models learning from speech
Marianne de Heer Kloots, Martijn Bentum, Hosein Mohebbi +3
Self-supervised speech models learn effective representations of spoken language, which have been shown to reflect various aspects of linguistic structure. But when does such struc…
What do self-supervised speech models know about Dutch? Analyzing advantages of language-specific pre-training
Marianne de Heer Kloots, Hosein Mohebbi, Charlotte Pouw +3
How language-specific are speech representations learned by self-supervised models? Existing work has shown that a range of linguistic features can be successfully decoded from end…
Exploring bat song syllable representations in self-supervised audio encoders
Marianne de Heer Kloots, Mirjam Knörnschild
How well can deep learning models trained on human-generated sounds distinguish between another species' vocalization types? We analyze the encoding of bat song syllables in severa…
Human-like Linguistic Biases in Neural Speech Models: Phonetic Categorization and Phonotactic Constraints in Wav2Vec2.0
Marianne de Heer Kloots, Willem Zuidema
What do deep neural speech models know about phonology? Existing work has examined the encoding of individual linguistic units such as phonemes in these models. Here we investigate…
Vision-Language Models Align with Human Neural Representations in Concept Processing
Anna Bavaresco, Marianne de Heer Kloots, Sandro Pezzelle +1
Recent studies suggest that transformer-based vision-language models (VLMs) capture the multimodality of concept processing in the human brain. However, a systematic evaluation exp…
Perception of Phonological Assimilation by Neural Speech Recognition Models
Charlotte Pouw, Marianne de Heer Kloots, Afra Alishahi +1
Human listeners effortlessly compensate for phonological changes during speech perception, often unconsciously inferring the intended sounds. For example, listeners infer the under…
ChapGTP, ILLC's Attempt at Raising a BabyLM: Improving Data Efficiency by Automatic Task Formation
Jaap Jumelet, Michael Hanna, Marianne de Heer Kloots +3
We present the submission of the ILLC at the University of Amsterdam to the BabyLM challenge (Warstadt et al., 2023), in the strict-small track. Our final model, ChapGTP, is a mask…