EARSHOT: A Minimal Neural Network Model of Incremental Human Speech Recognition

James S. Magnuson, Heejo You, Sahil Luthra, Monica Li, Hosung Nam, Monty Escabí, Kevin Brown, Paul D. Allopenna, Rachel M. Theodore, Nicholas Monto, Jay G. Rueckl

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)


Despite the lack of invariance problem (the many-to-many mapping between acoustics and percepts), human listeners experience phonetic constancy and typically perceive what a speaker intends. Most models of human speech recognition (HSR) have side-stepped this problem, working with abstract, idealized inputs and deferring the challenge of working with real speech. In contrast, carefully engineered deep learning networks allow robust, real-world automatic speech recognition (ASR). However, the complexities of deep learning architectures and training regimens make it difficult to use them to provide direct insights into mechanisms that may support HSR. In this brief article, we report preliminary results from a two-layer network that borrows one element from ASR, long short-term memory nodes, which provide dynamic memory for a range of temporal spans. This allows the model to learn to map real speech from multiple talkers to semantic targets with high accuracy, with human-like timecourse of lexical access and phonological competition. Internal representations emerge that resemble phonetically organized responses in human superior temporal gyrus, suggesting that the model develops a distributed phonological code despite no explicit training on phonetic or phonemic targets. The ability to work with real speech is a major advance for cognitive models of HSR.

Original languageEnglish
Article numbere12823
JournalCognitive Science
Issue number4
Publication statusPublished - 2020 Apr 1


  • Computational modeling
  • Human speech recognition
  • Neurobiology of language

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience
  • Artificial Intelligence


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