461 citations · 1.1k across the 30 of their papers we have counts for
26 papers · 1 filter
Is the Language Familiarity Effect gradual? A computational modelling approach
Maureen de Seyssel, Guillaume Wisniewski, Emmanuel Dupoux
According to the Language Familiarity Effect (LFE), people are better at discriminating between speakers of their native language. Although this cognitive effect was largely studie…
Generative Spoken Language Modeling from Raw Audio
Kushal Lakhotia, Evgeny Kharitonov, Wei-Ning Hsu +8
We introduce Generative Spoken Language Modeling, the task of learning the acoustic and linguistic characteristics of a language from raw audio (no text, no labels), and a set of m…
VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation
Changhan Wang, Morgane Rivière, Ann Lee +6
We introduce VoxPopuli, a large-scale multilingual corpus providing 100K hours of unlabelled speech data in 23 languages. It is the largest open data to date for unsupervised repre…
The Zero Resource Speech Benchmark 2021: Metrics and baselines for unsupervised spoken language modeling
Tu Anh Nguyen, Maureen de Seyssel, Patricia Rozé +5
We introduce a new unsupervised task, spoken language modeling: the learning of linguistic representations from raw audio signals without any labels, along with the Zero Resource S…
The Zero Resource Speech Challenge 2020: Discovering discrete subword and word units
Ewan Dunbar, Julien Karadayi, Mathieu Bernard +6
We present the Zero Resource Speech Challenge 2020, which aims at learning speech representations from raw audio signals without any labels. It combines the data sets and metrics f…
Analogies minus analogy test: measuring regularities in word embeddings
Louis Fournier, Emmanuel Dupoux, Ewan Dunbar
Vector space models of words have long been claimed to capture linguistic regularities as simple vector translations, but problems have been raised with this claim. We decompose an…
"LazImpa": Lazy and Impatient neural agents learn to communicate efficiently
Mathieu Rita, Rahma Chaabouni, Emmanuel Dupoux
Previous work has shown that artificial neural agents naturally develop surprisingly non-efficient codes. This is illustrated by the fact that in a referential game involving a spe…
Compositionality and Generalization in Emergent Languages
Rahma Chaabouni, Eugene Kharitonov, Diane Bouchacourt +2
Natural language allows us to refer to novel composite concepts by combining expressions denoting their parts according to systematic rules, a property known as \emph{compositional…
Identification of primary and collateral tracks in stuttered speech
Rachid Riad, Anne-Catherine Bachoud-Lévi, Frank Rudzicz +1
Disfluent speech has been previously addressed from two main perspectives: the clinical perspective focusing on diagnostic, and the Natural Language Processing (NLP) perspective ai…
Seshat: A tool for managing and verifying annotation campaigns of audio data
Hadrien Titeux, Rachid Riad, Xuan-Nga Cao +5
We introduce Seshat, a new, simple and open-source software to efficiently manage annotations of speech corpora. The Seshat software allows users to easily customise and manage ann…
Libri-Light: A Benchmark for ASR with Limited or No Supervision
Jacob Kahn, Morgane Rivière, Weiyi Zheng +12
We introduce a new collection of spoken English audio suitable for training speech recognition systems under limited or no supervision. It is derived from open-source audio books f…
Neural language modeling of free word order argument structure
Charlotte Rochereau, Benoît Sagot, Emmanuel Dupoux
Neural language models trained with a predictive or masked objective have proven successful at capturing short and long distance syntactic dependencies. Here, we focus on verb argu…
Word-order biases in deep-agent emergent communication
Rahma Chaabouni, Eugene Kharitonov, Alessandro Lazaric +2
Sequence-processing neural networks led to remarkable progress on many NLP tasks. As a consequence, there has been increasing interest in understanding to what extent they process…
Anti-efficient encoding in emergent communication
Rahma Chaabouni, Eugene Kharitonov, Emmanuel Dupoux +1
Despite renewed interest in emergent language simulations with neural networks, little is known about the basic properties of the induced code, and how they compare to human langua…
The Zero Resource Speech Challenge 2019: TTS without T
Ewan Dunbar, Robin Algayres, Julien Karadayi +10
We present the Zero Resource Speech Challenge 2019, which proposes to build a speech synthesizer without any text or phonetic labels: hence, TTS without T (text-to-speech without t…
End-to-End Speech Recognition From the Raw Waveform
Neil Zeghidour, Nicolas Usunier, Gabriel Synnaeve +2
State-of-the-art speech recognition systems rely on fixed, hand-crafted features such as mel-filterbanks to preprocess the waveform before the training pipeline. In this paper, we…
Sampling strategies in Siamese Networks for unsupervised speech representation learning
Rachid Riad, Corentin Dancette, Julien Karadayi +3
Recent studies have investigated siamese network architectures for learning invariant speech representations using same-different side information at the word level. Here we invest…
Bayesian Models for Unit Discovery on a Very Low Resource Language
Lucas Ondel, Pierre Godard, Laurent Besacier +7
Developing speech technologies for low-resource languages has become a very active research field over the last decade. Among others, Bayesian models have shown some promising resu…
Linguistic unit discovery from multi-modal inputs in unwritten languages: Summary of the "Speaking Rosetta" JSALT 2017 Workshop
Odette Scharenborg, Laurent Besacier, Alan Black +16
We summarize the accomplishments of a multi-disciplinary workshop exploring the computational and scientific issues surrounding the discovery of linguistic units (subwords and word…
Are words easier to learn from infant- than adult-directed speech? A quantitative corpus-based investigation
Adriana Guevara-Rukoz, Alejandrina Cristia, Bogdan Ludusan +4
We investigate whether infant-directed speech (IDS) could facilitate word form learning when compared to adult-directed speech (ADS). To study this, we examine the distribution of…
The Zero Resource Speech Challenge 2017
Ewan Dunbar, Xuan Nga Cao, Juan Benjumea +5
We describe a new challenge aimed at discovering subword and word units from raw speech. This challenge is the followup to the Zero Resource Speech Challenge 2015. It aims at const…
Learning Filterbanks from Raw Speech for Phone Recognition
Neil Zeghidour, Nicolas Usunier, Iasonas Kokkinos +3
We train a bank of complex filters that operates on the raw waveform and is fed into a convolutional neural network for end-to-end phone recognition. These time-domain filterbanks…
Learning weakly supervised multimodal phoneme embeddings
Rahma Chaabouni, Ewan Dunbar, Neil Zeghidour +1
Recent works have explored deep architectures for learning multimodal speech representation (e.g. audio and images, articulation and audio) in a supervised way. Here we investigate…
Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies
Tal Linzen, Emmanuel Dupoux, Yoav Goldberg
The success of long short-term memory (LSTM) neural networks in language processing is typically attributed to their ability to capture long-distance statistical regularities. Ling…