ArticlePLoS computational biology2013
From birdsong to human speech recognition: bayesian inference on a hierarchy of nonlinear dynamical systems.
Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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31 citing papers in PubMed, 54 citations in OpenAlex.
- Dynamic encoding of speech sequence probability in human temporal cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2015Trial
- From pixels to planning: scale-free active inference.Frontiers in network physiology · 2025Article
- Convergent neural signatures of speech prediction error are a biological marker for spoken word recognition.Nature communications · 2024Article
- Fast frequency modulation is encoded according to the listener expectations in the human subcortical auditory pathway.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Federated inference and belief sharing.Neuroscience and biobehavioral reviews · 2024Review
- Rhythmic modulation of prediction errors: A top-down gating role for the beta-range in speech processing.PLoS computational biology · 2023Article
- How the conception of control influences our understanding of actions.Nature reviews. Neuroscience · 2023Review
- A deep hierarchy of predictions enables online meaning extraction in a computational model of human speech comprehension.PLoS biology · 2023Article
- Stochastic Chaos and Markov Blankets.Entropy (Basel, Switzerland) · 2021Article
- Active Inference and Cooperative Communication: An Ecological Alternative to the Alignment View.Frontiers in psychology · 2021Article
- Neuronal Sequence Models for Bayesian Online Inference.Frontiers in artificial intelligence · 2021Review
- COSMO-Onset: A Neurally-Inspired Computational Model of Spoken Word Recognition, Combining Top-Down Prediction and Bottom-Up Detection of Syllabic Onsets.Frontiers in systems neuroscience · 2021Article
- Combining predictive coding and neural oscillations enables online syllable recognition in natural speech.Nature communications · 2020Article
- A World Unto Itself: Human Communication as Active Inference.Frontiers in psychology · 2020Article
- Review
- Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data.Entropy (Basel, Switzerland) · 2019Article
- Applied physics: A new spin on nanoscale computing.Nature · 2017Article
- Article
- Article
- Perceptual Plasticity for Auditory Object Recognition.Frontiers in psychology · 2017Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Our knowledge about the computational mechanisms underlying human learning and recognition of sound sequences, especially speech, is still very limited. One difficulty in deciphering the exact means by which humans recognize speech is that there are scarce experimental findings at a neuronal, microscopic level. Here, we show that our neuronal-computational understanding of speech learning and recognition may be vastly improved by looking at an animal model, i.e., the songbird, which faces the same challenge as humans: to learn and decode complex auditory input, in an online fashion. Motivated by striking similarities between the human and songbird neural recognition systems at the macroscopic level, we assumed that the human brain uses the same computational principles at a microscopic level and translated a birdsong model into a novel human sound learning and recognition model with an emphasis on speech. We show that the resulting Bayesian model with a hierarchy of nonlinear dynamical systems can learn speech samples such as words rapidly and recognize them robustly, even in adverse conditions. In addition, we show that recognition can be performed even when words are spoken by different speakers and with different accents-an everyday situation in which current state-of-the-art speech recognition models often fail. The model can also be used to qualitatively explain behavioral data on human speech learning and derive predictions for future experiments.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.