Bibliography

Major publications by the team in recent years

• 1E. Dupoux.
  
  Cognitive Science in the era of Artificial Intelligence: A roadmap for reverse-engineering the infant language-learner, in: Cognition, 2018.
• 2A. Fourtassi, E. Dupoux.
  
  A Rudimentary Lexicon and Semantics Help Bootstrap Phoneme Acquisition, in: Proceedings of the 18th Conference on Computational Natural Language Learning (CoNLL), Baltimore, Maryland USA, Association for Computational Linguistics, June 2014, pp. 191-200. [ DOI : 10.3115/v1/W14-1620 ]
• 3A. Fourtassi, T. Schatz, B. Varadarajan, E. Dupoux.
  
  Exploring the Relative Role of Bottom-up and Top-down Information in Phoneme Learning, in: Proceedings of the 52nd Annual meeting of the ACL, Baltimore, Maryland, Association for Computational Linguistics, 2014, vol. 2, pp. 1-6. [ DOI : 10.3115/v1/P14-2001 ]
• 4Y. Hoshen, R. J. Weiss, K. W. Wilson.
  
  Speech acoustic modeling from raw multichannel waveforms, in: Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on, IEEE, 2015, pp. 4624–4628.
• 5T. Linzen, E. Dupoux, Y. Goldberg.
  
  Assessing the ability of LSTMs to learn syntax-sensitive dependencies, in: Transactions of the Association for Computational Linguistics, 2016, vol. 4, pp. 521-535.
• 6T. Linzen, E. Dupoux, B. Spector.
  
  Quantificational features in distributional word representations, in: Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics, 2016, pp. 1-11. [ DOI : 10.18653/v1/S16-2001 ]
• 7A. Martin, S. Peperkamp, E. Dupoux.
  
  Learning Phonemes with a Proto-lexicon, in: Cognitive Science, 2013, vol. 37, pp. 103-124. [ DOI : 10.1111/j.1551-6709.2012.01267.x ]
• 8S. Mehri, K. Kumar, I. Gulrajani, R. Kumar, S. Jain, J. Sotelo, A. Courville, Y. Bengio.
  
  SampleRNN: An unconditional end-to-end neural audio generation model, in: arXiv preprint arXiv:1612.07837, 2016.
• 9T. N. Sainath, R. J. Weiss, A. Senior, K. W. Wilson, O. Vinyals.
  
  Learning the speech front-end with raw waveform CLDNNs, in: Sixteenth Annual Conference of the International Speech Communication Association, 2015.
• 10T. Schatz, V. Peddinti, F. Bach, A. Jansen, H. Hynek, E. Dupoux.
  
  Evaluating speech features with the Minimal-Pair ABX task: Analysis of the classical MFC/PLP pipeline, in: INTERSPEECH-2013, Lyon, France, International Speech Communication Association, 2013, pp. 1781-1785.
• 11R. Thiollière, E. Dunbar, G. Synnaeve, M. Versteegh, E. Dupoux.
  
  A Hybrid Dynamic Time Warping-Deep Neural Network Architecture for Unsupervised Acoustic Modeling, in: INTERSPEECH-2015, 2015, pp. 3179-3183.
• 12A. Van Den Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. Senior, K. Kavukcuoglu.
  
  Wavenet: A generative model for raw audio, in: CoRR abs/1609.03499, 2016.

Publications of the year

Doctoral Dissertations and Habilitation Theses

• 13N. Zeghidour.
  
  Learning representations of speech from the raw waveform, PSL Research University, March 2019.
  
  https://tel.archives-ouvertes.fr/tel-02278616

Articles in International Peer-Reviewed Journals

• 14M. Bernard, R. Thiollière, A. Saksida, G. Loukatou, E. Larsen, M. C. Johnson, L. Fibla, E. Dupoux, R. Daland, X.-N. Cao, A. Cristia.
  
  WordSeg: Standardizing unsupervised word form segmentation from text, in: Behavior Research Methods, April 2019. [ DOI : 10.3758/s13428-019-01223-3 ]
  
  https://hal.archives-ouvertes.fr/hal-02274072
• 15A. Cristia, E. Dupoux, N. Bernstein Ratner, M. Soderstrom.
  
  Segmentability Differences Between Child-Directed and Adult-Directed Speech: A Systematic Test With an Ecologically Valid Corpus, in: Open Mind, 2019, vol. 3, pp. 13-22. [ DOI : 10.1162/opmi_a_00022 ]
  
  https://hal.archives-ouvertes.fr/hal-02274050
• 16E. Dunbar.
  
  Generative grammar, neural networks, and the implementational mapping problem: Response to Pater, in: Language, 2019, vol. 95, n^o 1, pp. e87-e98. [ DOI : 10.1353/lan.2019.0013 ]
  
  https://hal.archives-ouvertes.fr/hal-02274522
• 17M. Maldonado, E. Dunbar, E. Chemla.
  
  Mouse tracking as a window into decision making, in: Behavior Research Methods, June 2019, vol. 51, n^o 3, pp. 1085-1101. [ DOI : 10.3758/s13428-018-01194-x ]
  
  https://hal.archives-ouvertes.fr/hal-02274523

International Conferences with Proceedings

• 18R. Chaabouni, E. Kharitonov, A. Lazaric, E. Dupoux, M. Baroni.
  
  Word-order biases in deep-agent emergent communication, in: ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, July 2019, https://arxiv.org/abs/1905.12330.
  
  https://hal.archives-ouvertes.fr/hal-02274157
• 19E. Dunbar, R. Algayres, J. Karadayi, M. Bernard, J. Benjumea, X.-N. Cao, L. Miskic, C. Dugrain, L. Ondel, A. W. Black, L. Besacier, S. Sakti, E. Dupoux.
  
  The Zero Resource Speech Challenge 2019: TTS without T, in: Interspeech 2019 - 20th Annual Conference of the International Speech Communication Association, Graz, Austria, September 2019.
  
  https://hal.archives-ouvertes.fr/hal-02274112
• 20A. Fourtassi, E. Dupoux.
  
  Phoneme learning is influenced by the taxonomic organization of the semantic referents, in: Cognitive Science Society, Montreal, Canada, July 2019.
  
  https://hal.archives-ouvertes.fr/hal-02274093
• 21E. Kharitonov, R. Chaabouni, D. Bouchacourt, M. Baroni.
  
  EGG: a toolkit for research on Emergence of lanGuage in Games, in: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations, Hong Kong, China, November 2019, https://arxiv.org/abs/1907.00852. [ DOI : 10.18653/v1/D19-3010 ]
  
  https://hal.archives-ouvertes.fr/hal-02274229
• 22R. T. Mccoy, T. Linzen, E. Dunbar, P. Smolensky.
  
  RNNs Implicitly Implement Tensor Product Representations, in: ICLR 2019 - International Conference on Learning Representations, New Orleans, United States, May 2019, https://arxiv.org/abs/1812.08718 - Accepted to ICLR 2019.
  
  https://hal.archives-ouvertes.fr/hal-02274498
• 23J. Millet, N. Jurov, E. Dunbar.
  
  Comparing unsupervised speech learning directly to human performance in speech perception, in: CogSci 2019 - 41st Annual Meeting of Cognitive Science Society, Montréal, Canada, July 2019.
  
  https://hal.archives-ouvertes.fr/hal-02274499
• 24J. Millet, N. Zeghidour.
  
  Learning to detect dysarthria from raw speech, in: ICASSP-2019 - IEEE International Conference on Acoustics, Speech and Signal Processing, Brighton, United Kingdom, May 2019, https://arxiv.org/abs/1811.11101.
  
  https://hal.archives-ouvertes.fr/hal-02274504

Other Publications

• 25R. Chaabouni, E. Kharitonov, E. Dupoux, M. Baroni.
  
  Anti-efficient encoding in emergent communication, August 2019, https://arxiv.org/abs/1905.12561 - working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-02274205
• 26P. García, J. Villalba, H. Bredin, J. Du, D. Castan, A. Cristia, L. Bullock, L. Guo, K. Okabe, P. S. Nidadavolu, S. Kataria, S. Chen, L. Galmant, M. Lavechin, L. Sun, M.-P. Gill, B. Ben-Yair, S. Abdoli, X. Wang, W. Bouaziz, H. Titeux, E. Dupoux, K. A. Lee, N. Dehak.
  
  Speaker detection in the wild: Lessons learned from JSALT 2019, December 2019, https://arxiv.org/abs/1912.00938 - Submitted to ICASSP 2020.
  
  https://hal.archives-ouvertes.fr/hal-02417632
• 27J. Kahn, M. Rivière, W. Zheng, E. Kharitonov, Q. Xu, P.-E. Mazaré, J. Karadayi, V. Liptchinsky, R. Collobert, C. Fuegen, T. Likhomanenko, G. Synnaeve, A. Joulin, A. Mohamed, E. Dupoux.
  
  Libri-Light: A Benchmark for ASR with Limited or No Supervision, December 2019, https://arxiv.org/abs/1912.07875 - working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-02417621
• 28R. Riochet, M. Y. Castro, M. Bernard, A. Lerer, R. Fergus, V. Izard, E. Dupoux.
  
  IntPhys: A Benchmark for Visual Intuitive Physics Reasoning, August 2019, https://arxiv.org/abs/1803.07616 - working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-02274273
• 29C. Rochereau, B. Sagot, E. Dupoux.
  
  Modeling German Verb Argument Structures: LSTMs vs. Humans, December 2019, https://arxiv.org/abs/1912.00239 - working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-02417640

References in notes

• 30D. A. Ferrucci.
  
  Introduction to “this is watson”, in: IBM Journal of Research and Development, 2012, vol. 56, n^o 3.4, pp. 1–1.
• 31K. He, X. Zhang, S. Ren, J. Sun.
  
  Delving deep into rectifiers: Surpassing human-level performance on imagenet classification, in: Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 1026–1034.
• 32J. Hernández-Orallo, F. Martínez-Plumed, U. Schmid, M. Siebers, D. L. Dowe.
  
  Computer models solving intelligence test problems: Progress and implications, in: Artificial Intelligence, 2016, vol. 230, pp. 74–107.
• 33B. M. Lake, T. D. Ullman, J. B. Tenenbaum, S. J. Gershman.
  
  Building machines that learn and think like people, in: arXiv preprint arXiv:1604.00289, 2016.
• 34C. Lu, X. Tang.
  
  Surpassing human-level face verification performance on LFW with GaussianFace, in: arXiv preprint arXiv:1404.3840, 2014.
• 35S. T. Mueller.
  
  A partial implementation of the BICA cognitive decathlon using the Psychology Experiment Building Language (PEBL), in: International Journal of Machine Consciousness, 2010, vol. 2, n^o 02, pp. 273–288.
• 36D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, D. Hassabis.
  
  Mastering the game of Go with deep neural networks and tree search, in: Nature, 2016, vol. 529, n^o 7587, pp. 484–489.
• 37I. Sutskever, O. Vinyals, Q. V. Le.
  
  Sequence to sequence learning with neural networks, in: Advances in neural information processing systems, 2014, pp. 3104–3112.
• 38A. M. Turing.
  
  Computing machinery and intelligence, in: Mind, 1950, vol. 59, n^o 236, pp. 433–460.
• 39W. Xiong, J. Droppo, X. Huang, F. Seide, M. Seltzer, A. Stolcke, D. Yu, G. Zweig.
  
  Achieving human parity in conversational speech recognition, in: arXiv preprint arXiv:1610.05256, 2016.