A Selection of Literature on Models

  • Bernd J. Kröger
  • Trevor Bekolay


In this section models of speech production, perception, and learning are discussed. First, we present theoretical models based on gross brain activity data and behavioral data. We then describe quantitative computational models involving simulated brain activity or behavior.


Computer simulation Data-driven model Large-scale neural model Dual-route model Motor planning Motor execution 


  1. Bekolay T (2016) Biologically inspired methods in speech recognition and synthesis: closing the loop. Ph.D. thesis. University of Waterloo, CanadaGoogle Scholar
  2. Dell GS (1988) The retrieval of phonological forms in production: tests of predictions from a connectionist model. J Mem Lang 27:124–142CrossRefGoogle Scholar
  3. Dell GS, Schwartz MF, Martin N, Saffran EM, Gagnon DA (1997) Lexical access in aphasic and nonaphasic speakers. Psychol Rev 104:801–838CrossRefGoogle Scholar
  4. Guenther FH (2006) Cortical interactions underlying the production of speech sounds. J Commun Disord 39:350–365CrossRefGoogle Scholar
  5. Guenther FH, Vladusich T (2012) A neural theory of speech acquisition and production. J Neurolinguistics 25:408–422CrossRefGoogle Scholar
  6. Hickok G, Poeppel D (2007) The cortical organization of speech processing. Nat Rev Neurosci 8:393–402CrossRefGoogle Scholar
  7. Hinton G, Deng L, Yu D, Dahl GE, Abdel-Rahman M, Jaitly N, Senior A, Vanhoucke V, Nguyen P, Sainath TN, Kingsbury B (2012) Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag 29:82–97CrossRefGoogle Scholar
  8. Levelt WJM (1989) Speaking: from intention to articulation. MIT Press, CambridgeGoogle Scholar
  9. Levelt WJM, Roelofs A, Meyer AS (1999) A theory of lexical access in speech production. Behav Brain Sci 22:1–75PubMedGoogle Scholar
  10. Li P, Zhao X (2013) Self-organizing map models of language acquisition. Front Psychol 4:828CrossRefGoogle Scholar
  11. Li P, Farkas I, MacWhinney B (2004) Early lexical development in a self-organizing neural network. Neural Netw 17:1345–1362CrossRefGoogle Scholar
  12. Li P, Zhao X, MacWhinney B (2007) Dynamic self-organization and early lexical development in children. Cogn Sci 31:581–612CrossRefGoogle Scholar
  13. Ling ZH, Kang SY, Zen H, Senior A, Schuster M, Qian XJ, Meng H, Deng L (2015) Deep learning for acoustic modeling in parametric speech generation. A systematic review of existing techniques and future trends. IEEE Signal Process Mag 32:35–52CrossRefGoogle Scholar
  14. McClelland JL, Elman JL (1986) The TRACE model of speech perception. Cogn Psychol 18:1–86CrossRefGoogle Scholar
  15. Postma A (2000) Detection of errors during speech production: a review of speech monitoring models. Cognition 77:97–131CrossRefGoogle Scholar
  16. Price CJ, Crinion JT, MacSweeney M (2011) A generative model of speech production in Broca’s and Wernicke’s areas. Front Psychol 2:237CrossRefGoogle Scholar
  17. Riecker A, Mathiak K, Wildgruber D, Erb M, Hertrich I, Grodd W, Ackermann H (2005) fMRI reveals two distinct cerebral networks subserving speech motor control. Neurology 64:700–706CrossRefGoogle Scholar
  18. Roelofs A (1997) The WEAVER model of word-form encoding in speech production. Cognition 64:249–284CrossRefGoogle Scholar
  19. Warlaumont AS, Finnegan MK (2016) Learning to produce syllabic speech sounds via reward-modulated neural plasticity. PLoS One 11(1):e0145096. CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Bernd J. Kröger
    • 1
  • Trevor Bekolay
    • 2
  1. 1.Department of Phoniatrics, Pedaudiology and Communications DisordersRWTH Aachen UniversityAachenGermany
  2. 2.Applied Brain ResearchWaterlooCanada

Personalised recommendations