Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Parallel hierarchical encoding of linguistic representations in the human auditory cortex and recurrent automatic speech recognition systems

Nature Machine Intelligence (2026)Cite this article

Subjects

A preprint version of the article is available at bioRxiv.

Abstract

Transforming continuous acoustic speech signals into discrete linguistic meaning is a remarkable computational feat accomplished by both the human brain and modern artificial intelligence. A key scientific question is whether these biological and artificial systems, despite their different architectures, converge on similar strategies to solve this challenge. Although automatic speech recognition systems now achieve human-level performance, research on their parallels with the brain has been limited by biologically implausible, non-causal models and comparisons that stop at predicting brain activity without detailing the alignment of the underlying representations. Furthermore, studies using text-based models overlook the crucial acoustic stages of speech processing. Here we bridge these gaps by uncovering a striking correspondence between the brain’s processing hierarchy and the model’s internal representations using high-resolution intracranial recordings and a causal, recurrent automatic speech recognition model. Specifically, we demonstrate a deep alignment in their algorithmic approach: neural activity in distinct cortical regions maps topographically to corresponding model layers, and critically, the representational content at each stage follows a parallel progression from acoustic to phonetic, lexical and semantic information. This work thus moves beyond demonstrating simple model–brain alignment to specifying the shared underlying representations at each stage of processing, providing direct evidence that both systems converge on a similar computational strategy for transforming sound into meaning.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Stages of speech processing in the brain based on ASR modelling.
Fig. 2: Similar node-level linguistic encoding across the brain and ASR.
Fig. 3: Population-level linguistic encoding in the ASR.
Fig. 4: Effect of model training on linguistic representation.

Similar content being viewed by others

Data availability

Although the iEEG recordings used in this study cannot be made publicly available owing to patient privacy restrictions, they can be requested from the author (N.M.). Source data are provided with this paper.

Code availability

The code for preprocessing neural data, selecting responsive electrodes and creating brain plots is available in the naplib-python package91. A reproducible capsule can be found on CodeOcean92. The software contains tutorial notebooks and guides for preprocessing data as was done here.

References

  1. Stolcke, A. & Droppo, J. Comparing human and machine errors in conversational speech transcription. Preprint at https://doi.org/10.48550/arXiv.1708.08615 (2017).

  2. DeWitt, I. & Rauschecker, J. P. Phoneme and word recognition in the auditory ventral stream. Proc. Natl Acad. Sci. USA 109, E505–E514 (2012).

    Article  Google Scholar 

  3. Price, C. J. The anatomy of language: a review of 100 fMRI studies published in 2009. Ann. N. Y. Acad. Sci. 1191, 62–88 (2010).

    Article  Google Scholar 

  4. Poeppel, D. The neuroanatomic and neurophysiological infrastructure for speech and language. Curr. Opin. Neurobiol. 28, 142–149 (2014).

    Article  Google Scholar 

  5. Belinkov, Y. & Glass, J. Analyzing hidden representations in end-to-end automatic speech recognition systems. In Advances in Neural Information Processing Systems Vol. 30 (eds Guyon, I. et al.) (Curran Associates, 2017); https://proceedings.neurips.cc/paper_files/paper/2017/hash/b069b3415151fa7217e870017374de7c-Abstract.html

  6. Li, Y. et al. Dissecting neural computations in the human auditory pathway using deep neural networks for speech. Nat. Neurosci. 26, 2213–2225 (2023).

    Article  Google Scholar 

  7. Caucheteux, C., Gramfort, A. & King, J.-R. Evidence of a predictive coding hierarchy in the human brain listening to speech. Nat. Hum. Behav. 7, 430–441 (2023).

    Article  Google Scholar 

  8. Caucheteux, C. & King, J.-R. Brains and algorithms partially converge in natural language processing. Commun. Biol. 5, 134 (2022).

    Article  Google Scholar 

  9. Goldstein, A. et al. Shared computational principles for language processing in humans and deep language models. Nat. Neurosci. 25, 369–380 (2022).

    Article  Google Scholar 

  10. Hosseini, E. A. et al. Artificial neural network language models predict human brain responses to language even after a developmentally realistic amount of training. Neurobiol. Lang. 5, 43–63 (2024).

    Article  Google Scholar 

  11. Mischler, G., Li, Y. A., Bickel, S., Mehta, A. D. & Mesgarani, N. Contextual feature extraction hierarchies converge in large language models and the brain. Nat. Mach. Intell. 6, 1467–1477 (2024).

  12. Hadidi, N., Feghhi, E., Song, B. H., Blank, I. A. & Kao, J. C. Illusions of alignment between large language models and brains emerge from fragile methods and overlooked confounds. Preprint at bioRxiv https://doi.org/10.1101/2025.03.09.642245 (2025).

  13. Graves, A. Sequence transduction with recurrent neural networks. Preprint at https://doi.org/10.48550/arXiv.1211.3711 (2012).

  14. Ray, S. & Maunsell, J. H. Different origins of gamma rhythm and high-gamma activity in macaque visual cortex. PLoS Biol. 9, e1000610 (2011).

    Article  Google Scholar 

  15. Steinschneider, M., Fishman, Y. I. & Arezzo, J. C. Spectrotemporal analysis of evoked and induced electroencephalographic responses in primary auditory cortex (A1) of the awake monkey. Cereb. Cortex 18, 610–625 (2008).

    Article  Google Scholar 

  16. Fischl, B. et al. Automatically parcellating the human cerebral cortex. Cereb. Cortex 14, 11–22 (2004).

    Article  Google Scholar 

  17. He, Y. et al. Streaming end-to-end speech recognition for mobile devices. In ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing 6381–6385 (ICASSP, 2019); https://ieeexplore.ieee.org/abstract/document/8682336

  18. Rao, K., Sak, H. & Prabhavalkar, R. Exploring architectures, data and units for streaming end-to-end speech recognition with RNN-transducer. In 2017 IEEE Automatic Speech Recognition and Understanding Workshop 193–199 (ASRU, 2017); https://ieeexplore.ieee.org/abstract/document/8268935

  19. Li, J., Zhao, R., Hu, H. & Gong, Y. Improving RNN transducer modeling for end-to-end speech recognition. In 2019 IEEE Automatic Speech Recognition and Understanding Workshop 114–121 (ASRU; 2019); https://ieeexplore.ieee.org/abstract/document/9003906

  20. Shafey, L. E., Soltau, H. & Shafran, I. Joint speech recognition and speaker diarization via sequence transduction. Preprint at https://doi.org/10.48550/arXiv.1907.05337 (2019).

  21. Ghodsi, M., Liu, X., Apfel, J., Cabrera, R. & Weinstein, E. RNN-transducer with stateless prediction network. In ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing 7049–7053 (ICASSP, 2020); https://ieeexplore.ieee.org/abstract/document/9054419

  22. Stooke, A., Prabhavalkar, R., Sim, K. C. & Mengibar, P. M. Aligner-encoders: self-attention transformers can be self-transducers. Adv. Neural Inf. Process. Syst. 37, 100318–100340 (2024).

    Google Scholar 

  23. Gulati, A. et al. Conformer: convolution-augmented transformer for speech recognition. Preprint at https://doi.org/10.48550/arXiv.2005.08100 (2020).

  24. Keshishian, M. et al. Estimating and interpreting nonlinear receptive field of sensory neural responses with deep neural network models. eLife 9, e53445 (2020).

    Article  Google Scholar 

  25. Mischler, G., Keshishian, M., Bickel, S., Mehta, A. D. & Mesgarani, N. Deep neural networks effectively model neural adaptation to changing background noise and suggest nonlinear noise filtering methods in auditory cortex. NeuroImage 266, 119819 (2023).

    Article  Google Scholar 

  26. Saon, G. et al. Granite-speech: open-source speech-aware LLMs with strong English ASR capabilities. Preprint at https://doi.org/10.48550/arXiv.2505.08699 (2025).

  27. McGettigan, C. et al. An application of univariate and multivariate approaches in fMRI to quantifying the hemispheric lateralization of acoustic and linguistic processes. J. Cogn. Neurosci. 24, 636–652 (2012).

    Article  Google Scholar 

  28. Zatorre, R. J., Evans, A. C., Meyer, E. & Gjedde, A. Lateralization of phonetic and pitch discrimination in speech processing. Science 256, 846–849 (1992).

    Article  Google Scholar 

  29. Crosse, M. J., Di Liberto, G. M., Bednar, A. & Lalor, E. C. The multivariate temporal response function (mTRF) toolbox: a MATLAB toolbox for relating neural signals to continuous stimuli. Front. Hum. Neurosci. 10, 604 (2016).

    Article  Google Scholar 

  30. Keshishian, M. et al. Joint, distributed and hierarchically organized encoding of linguistic features in the human auditory cortex. Nat. Hum. Behav. 7, 740–753 (2023).

    Article  Google Scholar 

  31. Kornblith, S., Norouzi, M., Lee, H. & Hinton, G. Similarity of neural network representations revisited. In International Conference on Machine Learning (eds Chaudhuri, K. & Salakhutdinov, R.) 3519–3529 (PMLR, 2019).

  32. Stork. Is backpropagation biologically plausible? In International 1989 Joint Conference on Neural Networks (IEEE Xplore, 1989); https://ieeexplore.ieee.org/abstract/document/118705

  33. Betti, A., Gori, M. & Marra, G. Backpropagation and biological plausibility. Preprint at https://doi.org/10.48550/arXiv.1808.06934 (2018).

  34. Gilbert, C. D. & Sigman, M. Brain states: top-down influences in sensory processing. Neuron 54, 677–696 (2007).

    Article  Google Scholar 

  35. Kveraga, K., Ghuman, A. S. & Bar, M. Top-down predictions in the cognitive brain. Brain Cogn. 65, 145–168 (2007).

    Article  Google Scholar 

  36. Rahman, M., Willmore, B. D. B., King, A. J. & Harper, N. S. Simple transformations capture auditory input to cortex. Proc. Natl Acad. Sci. USA 117, 28442–28451 (2020).

    Article  Google Scholar 

  37. Gill, P., Zhang, J., Woolley, S. M. N., Fremouw, T. & Theunissen, F. E. Sound representation methods for spectro-temporal receptive field estimation. J. Comput. Neurosci. 21, 5–20 (2006).

    Article  Google Scholar 

  38. Tuckute, G., Feather, J., Boebinger, D. & McDermott, J. H. Many but not all deep neural network audio models capture brain responses and exhibit correspondence between model stages and brain regions. PLoS Biol. 21, e3002366 (2023).

    Article  Google Scholar 

  39. Nagamine, T. & Mesgarani, N. Understanding the representation and computation of multilayer perceptrons: a case study in speech recognition. In Proc. 34th International Conference on Machine Learning Vol. 70 (eds Precup, D. & Teh, Y. W.) 2564–2573 (PMLR, 2017); https://proceedings.mlr.press/v70/nagamine17a.html

  40. Nagamine, T., Seltzer, M. L. & Mesgarani, N. Exploring how deep neural networks form phonemic categories. In Interspeech, 1912–1916 (2015).

  41. Raymondaud, Q., Rouvier, M. & Dufour, R. Probing the information encoded in neural-based acoustic models of automatic speech recognition systems. Preprint at https://doi.org/10.48550/arXiv.2402.19443 (2024).

  42. Theunissen, F. E., Sen, K. & Doupe, A. J. Spectral–temporal receptive fields of nonlinear auditory neurons obtained using natural sounds. J. Neurosci. 20, 2315–2331 (2000).

    Article  Google Scholar 

  43. Theunissen, F. E. et al. Estimating spatio-temporal receptive fields of auditory and visual neurons from their responses to natural stimuli. Netw., Comput. Neural Syst. 12, 289 (2001).

    Article  Google Scholar 

  44. Hickok, G. & Poeppel, D. The cortical organization of speech processing. Nat. Rev. Neurosci. 8, 393–402 (2007).

    Article  Google Scholar 

  45. Hickok, G. & Poeppel, D. Dorsal and ventral streams: a framework for understanding aspects of the functional anatomy of language. Cognition 92, 67–99 (2004).

    Article  Google Scholar 

  46. Friederici, A. D. The cortical language circuit: from auditory perception to sentence comprehension. Trends Cogn. Sci. 16, 262–268 (2012).

    Article  Google Scholar 

  47. Pallier, C., Devauchelle, A.-D. & Dehaene, S. Cortical representation of the constituent structure of sentences. Proc. Natl Acad. Sci. USA 108, 2522–2527 (2011).

    Article  Google Scholar 

  48. Fedorenko, E., Nieto-Castañon, A. & Kanwisher, N. Lexical and syntactic representations in the brain: an fMRI investigation with multi-voxel pattern analyses. Neuropsychologia 50, 499–513 (2012).

    Article  Google Scholar 

  49. Costafreda, S. G. et al. A systematic review and quantitative appraisal of fMRI studies of verbal fluency: role of the left inferior frontal gyrus. Hum. Brain Mapp. 27, 799–810 (2006).

    Article  Google Scholar 

  50. Rauschecker, J. P. Ventral and dorsal streams in the evolution of speech and language. Front. Evol. Neurosci. 4, 7 (2012).

    Article  Google Scholar 

  51. Matchin, W., Hammerly, C. & Lau, E. The role of the IFG and pSTS in syntactic prediction: evidence from a parametric study of hierarchical structure in fMRI. Cortex 88, 106–123 (2017).

    Article  Google Scholar 

  52. Hagoort, P. On Broca, brain, and binding: a new framework. Trends Cogn. Sci. 9, 416–423 (2005).

    Article  Google Scholar 

  53. Matchin, W. & Hickok, G. The cortical organization of syntax. Cereb. Cortex 30, 1481–1498 (2020).

    Article  Google Scholar 

  54. Rogalsky, C. & Hickok, G. The role of Broca’s area in sentence comprehension. J. Cogn. Neurosci. 23, 1664–1680 (2011).

    Article  Google Scholar 

  55. Novick, J. M., Trueswell, J. C. & Thompson-Schill, S. L. Broca’s area and language processing: evidence for the cognitive control connection. Lang. Linguist. Compass 4, 906–924 (2010).

  56. January, D., Trueswell, J. C. & Thompson-Schill, S. L. Co-localization of stroop and syntactic ambiguity resolution in Broca’s area: implications for the neural basis of sentence processing. J. Cogn. Neurosci. 21, 2434–2444 (2009).

    Article  Google Scholar 

  57. Hasson, U., Yang, E., Vallines, I., Heeger, D. J. & Rubin, N. A hierarchy of temporal receptive windows in human cortex. J. Neurosci. 28, 2539–2550 (2008).

    Article  Google Scholar 

  58. Lerner, Y., Honey, C. J., Silbert, L. J. & Hasson, U. Topographic mapping of a hierarchy of temporal receptive windows using a narrated story. J. Neurosci. 31, 2906–2915 (2011).

    Article  Google Scholar 

  59. Ding, N. et al. Characterizing neural entrainment to hierarchical linguistic units using electroencephalography (EEG). Front. Hum. Neurosci. 11, 481 (2017).

    Article  Google Scholar 

  60. Keshishian, M., Norman-Haignere, S. & Mesgarani, N. Understanding adaptive, multiscale temporal integration in deep speech recognition systems. In Advances in Neural Information Processing Systems Vol. 34 (eds Ranzato, M. et al.) 24455–24467 (Curran Associates, 2021); https://proceedings.neurips.cc/paper/2021/hash/ccce2fab7336b8bc8362d115dec2d5a2-Abstract.html

  61. Dennis, M. & Whitaker, H. A. 8—hemispheric equipotentiality and language acquisition. In Language Development and Neurological Theory (eds Segalowitz, S. J. & Gruber, F. A.) 93–106 (Academic Press, 1977).; https://www.sciencedirect.com/science/article/pii/B9780126356502500145

  62. Millar, J. M. & Whitaker, H. A. Chapter 4—the right hemisphere’s contribution to language: a review of the evidence from brain-damaged subjects. In Language Functions and Brain Organization (ed. Segalowitz, S. J.) 87–113 (Academic Press, 1983).; https://www.sciencedirect.com/science/article/pii/B9780126356403500102

  63. Poeppel, D. Pure word deafness and the bilateral processing of the speech code. Cogn. Sci. 25, 679–693 (2001).

    Article  Google Scholar 

  64. Poeppel, D. The analysis of speech in different temporal integration windows: cerebral lateralization as ‘asymmetric sampling in time’. Speech Commun. 41, 245–255 (2003).

    Article  Google Scholar 

  65. Oderbolz, C., Poeppel, D. & Meyer, M. Asymmetric sampling in time: evidence and perspectives. Neurosci. Biobehav. Rev. 171, 106082 (2025).

    Article  Google Scholar 

  66. Tang, C., Hamilton, L. S. & Chang, E. F. Intonational speech prosody encoding in the human auditory cortex. Science 357, 797–801 (2017).

    Article  Google Scholar 

  67. Li, Y., Tang, C., Lu, J., Wu, J. & Chang, E. F. Human cortical encoding of pitch in tonal and non-tonal languages. Nat. Commun. 12, 1161 (2021).

    Article  Google Scholar 

  68. Edwards, E. et al. Comparison of time–frequency responses and the event-related potential to auditory speech stimuli in human cortex. J. Neurophysiol. 102, 377–386 (2009).

    Article  Google Scholar 

  69. Groppe, D. M. et al. iELVis: An open source MATLAB toolbox for localizing and visualizing human intracranial electrode data. J. Neurosci. Methods 281, 40–48 (2017).

    Article  Google Scholar 

  70. Jenkinson, M. & Smith, S. A global optimisation method for robust affine registration of brain images. Med. Image Anal. 5, 143–156 (2001).

    Article  Google Scholar 

  71. Jenkinson, M., Bannister, P., Brady, M. & Smith, S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage 17, 825–841 (2002).

    Article  Google Scholar 

  72. Smith, S. M. Fast robust automated brain extraction. Hum. Brain Mapp. 17, 143–155 (2002).

    Article  Google Scholar 

  73. Papademetris, X. et al. BioImage suite: an integrated medical image analysis suite: an update. Insight J. 2006, 209 (2006).

    Google Scholar 

  74. Destrieux, C., Fischl, B., Dale, A. & Halgren, E. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. NeuroImage 53, 1–15 (2010).

    Article  Google Scholar 

  75. Sweet, R. A., Dorph-Petersen, K.-A. & Lewis, D. A. Mapping auditory core, lateral belt, and parabelt cortices in the human superior temporal gyrus. J. Comp. Neurol. 491, 270–289 (2005).

    Article  Google Scholar 

  76. Hamilton, L. S., Oganian, Y., Hall, J. & Chang, E. F. Parallel and distributed encoding of speech across human auditory cortex. Cell 184, 4626–4639.e13 (2021).

    Article  Google Scholar 

  77. Ozker, M., Schepers, I. M., Magnotti, J. F., Yoshor, D. & Beauchamp, M. S. A double dissociation between anterior and posterior superior temporal gyrus for processing audiovisual speech demonstrated by electrocorticography. J. Cogn. Neurosci. 29, 1044–1060 (2017).

    Article  Google Scholar 

  78. Saon, G., Tüske, Z., Bolanos, D. & Kingsbury, B. Advancing RNN transducer technology for speech recognition. In ICASSP 2021–2021 IEEE International Conference on Acoustics, Speech and Signal Processing 5654–5658 (ICASSP, 2021); https://ieeexplore.ieee.org/abstract/document/9414716

  79. Masanori, M., Hideki, K. & Haruhiro, K. Fast and reliable f0 estimation method based on the period extraction of vocal fold vibration of singing voice and speech. J. Audio Eng. Soc. 57, 11 (2009).

    Google Scholar 

  80. Lenzo, K. The CMU pronouncing dictionary. Carnegie Mellon Univ. http://www.speech.cs.cmu.edu/cgi-bin/cmudict (2025).

  81. Brysbaert, M. & New, B. Moving beyond Kučera and Francis: a critical evaluation of current word frequency norms and the introduction of a new and improved word frequency measure for American English. Behav. Res. Methods 41, 977–990 (2009).

    Article  Google Scholar 

  82. Vitevitch, M. S. & Luce, P. A. Probabilistic phonotactics and neighborhood activation in spoken word recognition. J. Mem. Lang. 40, 374–408 (1999).

    Article  Google Scholar 

  83. Brodbeck, C., Hong, L. E. & Simon, J. Z. Rapid transformation from auditory to linguistic representations of continuous speech. Curr. Biol. 28, 3976–3983.e5 (2018).

    Article  Google Scholar 

  84. Ylinen, S. et al. Predictive coding of phonological rules in auditory cortex: a mismatch negativity study. Brain Lang. 162, 72–80 (2016).

    Article  Google Scholar 

  85. Friston, K. The free-energy principle: a rough guide to the brain? Trends Cogn. Sci. 13, 293–301 (2009).

    Article  Google Scholar 

  86. Gagnepain, P., Henson, R. N. & Davis, M. H. Temporal predictive codes for spoken words in auditory cortex. Curr. Biol. 22, 615–621 (2012).

    Article  Google Scholar 

  87. Leonard, M. K., Bouchard, K. E., Tang, C. & Chang, E. F. Dynamic encoding of speech sequence probability in human temporal cortex. J. Neurosci. 35, 7203–7214 (2015).

    Article  Google Scholar 

  88. Balota, D. A. et al. The English lexicon project. Behav. Res. Methods 39, 445–459 (2007).

    Article  Google Scholar 

  89. Shaoul, C. & Westbury, C. Exploring lexical co-occurrence space using HiDEx. Behav. Res. Methods 42, 393–413 (2010).

    Article  Google Scholar 

  90. Radford, A. et al. Language models are unsupervised multitask learners. OpenAI Blog 1, 9 (2019).

    Google Scholar 

  91. Mischler, G., Raghavan, V., Keshishian, M. & Mesgarani, N. naplib-python: neural acoustic data processing and analysis tools in python. Softw. Impacts 17, 100541 (2023).

    Article  Google Scholar 

  92. Mischler, G., Raghavan, V., Keshishian, M. & Mesgarani, N. naplib-python: neural acoustic data processing and analysis tools in python, version 1.0. CodeOcean https://doi.org/10.24433/CO.7346414.v1 (2023).

Download references

Acknowledgements

This study was funded by the National Institute on Deafness and Other Communication Disorders (grant no. R01DC014279 to N.M.). S.B. was also supported by the National Institute on Deafness and Other Communication Disorders, grant no. R01DC019979. The funders had no role in the study design, data collection and analysis, decision to publish and manuscript preparation.

Author information

Author notes

  1. These authors contributed equally: Menoua Keshishian, Gavin Mischler.

Authors and Affiliations

  1. Department of Electrical Engineering, Columbia University, New York, NY, USA

    Menoua Keshishian, Gavin Mischler & Nima Mesgarani

  2. Zuckerman Institute, Columbia University, New York, NY, USA

    Menoua Keshishian, Gavin Mischler & Nima Mesgarani

  3. IBM Research, Yorktown Heights, NY, USA

    Samuel Thomas & Brian Kingsbury

  4. The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA

    Stephan Bickel & Ashesh D. Mehta

  5. Department of Neurosurgery, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA

    Stephan Bickel & Ashesh D. Mehta

Authors
  1. Menoua Keshishian
    View author publications

    Search author on:PubMed Google Scholar

  2. Gavin Mischler
    View author publications

    Search author on:PubMed Google Scholar

  3. Samuel Thomas
    View author publications

    Search author on:PubMed Google Scholar

  4. Brian Kingsbury
    View author publications

    Search author on:PubMed Google Scholar

  5. Stephan Bickel
    View author publications

    Search author on:PubMed Google Scholar

  6. Ashesh D. Mehta
    View author publications

    Search author on:PubMed Google Scholar

  7. Nima Mesgarani
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Conceptualization (M.K. and N.M.); methodology (M.K., G.M., S.T., B.K. and N.M.); data collection (S.B. and A.D.M.); data analysis (M.K., G.M., S.T. and N.M.); writing—original draft (M.K. and N.M.); and writing—revision (G.M., S.T., B.K. and N.M.).

Corresponding author

Correspondence to Nima Mesgarani.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Machine Intelligence thanks Yuanning Li, Jonathan Venezia and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–3.

Source data

Source Data Fig. 1

Data for plotting Fig. 1.

Source Data Fig. 2

Data for plotting Fig. 2.

Source Data Fig. 3

Data for plotting Fig. 3.

Source Data Fig. 4

Data for plotting Fig. 4.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Keshishian, M., Mischler, G., Thomas, S. et al. Parallel hierarchical encoding of linguistic representations in the human auditory cortex and recurrent automatic speech recognition systems. Nat Mach Intell (2026). https://doi.org/10.1038/s42256-026-01185-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • DOI: https://doi.org/10.1038/s42256-026-01185-0

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing