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Self-supervised language models are very effective at predicting high-level cortical responses during language comprehension.
Towards a Unified Phonetic Theory
Lieberman, P · 1970
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Some Stages of Processing in Speech Perception
Pisoni, D. B. and Sawusch, J. R · 1975
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Cognitive penetration of the mechanisms of perception: Compensation for coarticulation of lexically restored phonemes
Elman, J. L. and McClelland, J. L · 1988
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Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction
Dale, A. M., Fischl, B., and Sereno, M. I · 1998
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Spectro-temporal modulation transfer functions and speech intelligibility
Chi, T., Gao, Y., Guyton, M. C., Ru, P., and Shamma, S · 1999
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Multiresolution spectrotemporal analysis of complex sounds
Chi, T., Ru, P., and Shamma, S. A · 2005
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Complete functional characterization of sensory neurons by system identification
Wu, M. C.-K., David, S. V., and Gallant, J. L · 2006
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The cortical organization of speech processing
Hickok, G. and Poeppel, D · 2007
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Where Is the Semantic System? A Critical Review and Meta-Analysis of 120 Functional Neuroimaging Studies
Binder, J. R., Desai, R. H., Graves, W. W., and Conant, L. L · 2009
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Phonetic Feature Encoding in Human Superior Temporal Gyrus
Mesgarani, N., Cheung, C., Johnson, K., and Chang, E. F · 2014
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Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., and Manning, C · 2014
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Aligning context-based statistical models of language with brain activity during reading
Wehbe, L., Vaswani, A., Knight, K., and Mitchell, T · 2014
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Pycortex: An interactive surface visualizer for fMRI
Gao, J. S., Huth, A. G., Lescroart, M. D., and Gallant, J. L · 2015
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Librispeech: An ASR corpus based on public domain audio books
Panayotov, V., Chen, G., Povey, D., and Khudanpur, S · 2015
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Deep speech 2: End-to-end speech recognition in English and mandarin
Amodei, D., Ananthanarayanan, S., Anubhai, R., Bai, J., Battenberg, E., Case, C., Casper, J., Catanzaro, B., Cheng, Q., Chen, G., Chen, J., Chen, J., Chen, Z., Chrzanowski, M., Coates, A., Diamos, G., Ding, K., Du, N., Elsen, E., Engel, J., Fang, W., Fan, L., Fougner, C., Gao, L., Gong, C., Hannun, A., Han, T., Johannes, L. V., Jiang, B., Ju, C., Jun, B., LeGresley, P., Lin, L., Liu, J., Liu, Y., Li, W., Li, X., Ma, D., Narang, S., Ng, A., Ozair, S., Peng, Y., Prenger, R., Qian, S., Quan, Z., Raiman, J., Rao, V., Satheesh, S., Seetapun, D., Sengupta, S., Srinet, K., Sriram, A., Tang, H., Tang, L., Wang, C., Wang, J., Wang, K., Wang, Y., Wang, Z., Wang, Z., Wu, S., Wei, L., Xiao, B., Xie, W., Xie, Y., Yogatama, D., Yuan, B., Zhan, J., and Zhu, Z · 2016
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Probing for semantic evidence of composition by means of simple classification tasks
Ettinger, A., Elgohary, A., and Resnik, P · 2016
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Natural speech reveals the semantic maps that tile human cerebral cortex
Huth, A. G., de Heer, W. A., Griffiths, T. L., Theunissen, F. E., and Gallant, J. L · 2016
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Does String-Based Neural MT Learn Source Syntax?
Shi, X., Padhi, I., and Knight, K · 2016
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Understanding intermediate layers using linear classifier probes
Alain, G. and Bengio, Y · 2017
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The Hierarchical Cortical Organization of Human Speech Processing
de Heer, W. A., Huth, A. G., Griffiths, T. L., Gallant, J. L., and Theunissen, F. E · 2017
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Incorporating Context into Language Encoding Models for fMRI
Jain, S. and Huth, A · 2018
Wav2vec: Unsupervised Pre-training for Speech Recognition
Schneider, S., Baevski, A., Collobert, R., and Auli, M · 2019
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Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)
Toneva, M. and Wehbe, L · 2019
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Wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
Baevski, A., Zhou, H., Mohamed, A., and Auli, M · 2020
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Generative Pre-Training for Speech with Autoregressive Predictive Coding
Chung, Y.-A. and Glass, J · 2020
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Thinking ahead: Spontaneous prediction in context as a keystone of language in humans and machines
Goldstein, A., Zada, Z., Buchnik, E., Schain, M., Price, A., Aubrey, B., Nastase, S. A., Feder, A., Emanuel, D., Cohen, A., Jansen, A., Gazula, H., Choe, G., Rao, A., Kim, S. C., Casto, C., Fanda, L., Doyle, W., Friedman, D., Dugan, P., Melloni, L., Reichart, R., Devore, S., Flinker, A., Hasenfratz, L., Levy, O., Hassidim, A., Brenner, M., Matias, Y., Norman, K. A., Devinsky, O., and Hasson, U · 2020
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Cited alongside, same era.
A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy
Kell, A. J. E., Yamins, D. L. K., Shook, E. N., Norman-Haignere, S. V., and McDermott, J. H · 2018
Cited alongside, same era.
Neural responses to natural and model-matched stimuli reveal distinct computations in primary and nonprimary auditory cortex
Norman-Haignere, S. V. and McDermott, J. H · 2018
Cited alongside, same era.
Deep Contextualized Word Representations
Peters, M. E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., and Zettlemoyer, L · 2018
Cited alongside, same era.
Improving Language Understanding by Generative Pre-Training
Radford, A., Narasimhan, K., Salimans, T., and Sutskever, I · 2018
Cited alongside, same era.
Hierarchy of speech-driven spectrotemporal receptive fields in human auditory cortex
Venezia, J. H., Thurman, S. M., Richards, V. M., and Hickok, G · 2018
Cited alongside, same era.
An Unsupervised Autoregressive Model for Speech Representation Learning
Chung, Y.-A., Hsu, W.-N., Tang, H., and Glass, J · 2019
Cited alongside, same era.
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Interpretable multi-timescale models for predicting fMRI responses to continuous natural speech
Jain, S., Vo, V., Mahto, S., LeBel, A., Turek, J. S., and Huth, A · 2020
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Transformers: State-of-the-Art Natural Language Processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., Davison, J., Shleifer, S., von Platen, P., Ma, C., Jernite, Y., Plu, J., Xu, C., Le Scao, T., Gugger, S., Drame, M., Lhoest, Q., and Rush, A · 2020
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GPT-2’s activations predict the degree of semantic comprehension in the human brain, September 2021
Caucheteux, C., Gramfort, A., and King, J.-R · 2021
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HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units
Hsu, W.-N., Bolte, B., Tsai, Y.-H. H., Lakhotia, K., Salakhutdinov, R., and Mohamed, A · 2021
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Voxelwise Encoding Models Show That Cerebellar Language Representations Are Highly Conceptual
LeBel, A., Jain, S., and Huth, A. G · 2021
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Inductive biases, pretraining and fine-tuning jointly account for brain responses to speech
Millet, J. and King, J.-R · 2021
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Layer-wise Analysis of a Self-supervised Speech Representation Model
Pasad, A., Chou, J.-C., and Livescu, K · 2021
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The neural architecture of language: Integrative modeling converges on predictive processing
Schrimpf, M., Blank, I. A., Tuckute, G., Kauf, C., Hosseini, E. A., Kanwisher, N., Tenenbaum, J. B., and Fedorenko, E · 2021
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SUPERB: Speech processing Universal PERformance Benchmark
Yang, S.-w., Chi, P.-H., Chuang, Y.-S., Lai, C.-I. J., Lakhotia, K., Lin, Y. Y., Liu, A. T., Shi, J., Chang, X., Lin, G.-T., Huang, T.-H., Tseng, W.-C., Lee, K.-t., Liu, D.-R., Huang, Z., Dong, S., Li, S.-W., Watanabe, S., Mohamed, A., and Lee, H.-y · 2021
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