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Das LS-Modell (Lexikon-Silbenspeicher-Modell)

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Zusammenfassung

In diesem Kapitel wird ein Ansatz zur Modellierung der Sprachverarbeitung und des Sprachlernens vorgestellt. Teile dieses Simulationsmodells liegen im STAA-Ansatz, andere Teile bereits im NEF vor. Das hier beschriebene Modell umfasst kognitive wie auch sensorisch-motorische Komponenten der Sprachproduktion und der Sprachwahrnehmung. Darüber hinaus wird der Aufbau des mentalen Lexikons und des mentalen Silbenspeichers durch Babbeln und Imitieren simuliert.

Literatur

  1. Birkholz P, Jackel D, Kröger BJ (2006) Construction and control of a three-dimensional vocal tract model. Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2006) (Toulouse, France), S 873–876Google Scholar
  2. Birkholz P, Jackel D, Kröger BJ (2007) Simulation of losses due to turbulence in the time-varying vocal system. IEEE Trans. Audio, Speech, Language Process 15:1218–1225CrossRefGoogle Scholar
  3. Birkholz P, Jackel D (2004) Influence of temporal discretization schemes on formant frequencies and bandwidths in time domain simulations of the vocal tract system. Proceedings of Interspeech 2004 (ICSLP, Jeju, Korea), S 1125–1128Google Scholar
  4. Birkholz P, Kröger BJ (2006) Vocal tract model adaptation using magnetic resonance imaging. Proceedings of the 7th International Seminar on Speech Production, Belo Horizonte, Brazil, S 493–500Google Scholar
  5. Cao M, Li A, Fang Q, Kaufmann E, Kröger BJ (2014) Interconnected growing self-organizing maps for auditory and semantic acquisition modeling. Front Psychol 5:236PubMedPubMedCentralGoogle Scholar
  6. Eliasmith C, Stewart TC, Choo X, Bekolay T, DeWolf T, Tan Y (2012) A large-scale model of the functioning brain. Science 338:1202–1205Google Scholar
  7. Eliasmith C (2013) How to build a brain. Oxford University Press, OxfordGoogle Scholar
  8. Kröger BJ, Kannampuzha J, Neuschaefer-Rube C (2009) Towards a neurocomputational model of speech production and perception. Speech Commun 51:793–809CrossRefGoogle Scholar
  9. Kröger BJ, Kannampuzha J, Kaufmann E (2014a) Associative learning and self-organization as basic principles for simulating speech acquisition, speech production, and speech perception. EPJ Nonlinear Biomed Phys 2:2CrossRefGoogle Scholar
  10. Kröger BJ, Bekolay T, Eliasmith C (2014b) Modeling speech production using the Neural Engineering Framework. Proceedings of CogInfoCom 2014, Vetri sul Mare, Italy, S 203–208 (ISBN: 978-1-4799-7279-1) and IEEE Xplore Digital Library DOI= https://doi.org/10.1109/CogInfoCom.2014.7020446
  11. Kröger BJ, Crawford E, Bekolay T, Eliasmith C (2016a) Modeling interactions between speech production and perception: speech error detection at semantic and phonological levels and the inner speech loop. Front Comput Neurosc 10:51CrossRefGoogle Scholar
  12. Kröger BJ, Bekolay T, Blouw P (2016b) Modeling motor planning in speech processing using the Neural Engineering Framework. In: Jokisch O (Hrsg) Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2016. TUDpress, Dresden, Germany, S 15–22Google Scholar
  13. Kröger BJ, Birkholz P (2007) A gesture-based concept for speech movement control in articulatory speech synthesis. In: Esposito A, Faundez-Zanuy M, Keller E, Marinaro M (Hrsg) Verbal and Nonverbal Communication Behaviours, LNAI 4775. Springer Verlag, Berlin, Heidelberg, S 174–189CrossRefGoogle Scholar
  14. Kröger BJ, Cao M (2015) The emergence of phonetic-phonological features in a biologically inspired model of speech processing. J Phonetics 53:88–100CrossRefGoogle Scholar
  15. Kröger BJ, Kannampuzha J (2008) A neurofunctional model of speech production including aspects of auditory and audio-visual speech perception. Proceedings of the International Conference on Audio-Visual Speech Processing 2008, Moreton Island, Queensland, Australia, S 83–88Google Scholar
  16. Markram H (2006) The blue brain project. Nat Rev Neurosci 7:153–160CrossRefPubMedGoogle Scholar
  17. Senft V, Stewart TC, Bekolay T, Eliasmith C, Kröger BJ (2016) Reduction of dopamine in basal ganglia and its effects on syllable sequencing in speech: A computer simulation study. Basal Ganglia 6:7–17Google Scholar

Copyright information

© Springer-Verlag GmbH Deutschland 2018

Authors and Affiliations

  1. 1.Klinik für Phoniatrie, Pädaudiologie und KommunikationsstörungenRWTH Aachen UniversityAachenDeutschland

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