An attention-gating recurrent working memory architecture for emergent speech representation

Mark Elshaw, Roger Moore, Michael Klein

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

This paper describes an attention-gating recurrent self-organising map approach for emergent speech representation. Inspired by evidence from human cognitive processing, the architecture combines two main neural components. The first component, the attention-gating mechanism, uses actor-critic learning to perform selective attention towards speech. Through this selective attention approach, the attention-gating mechanism controls access to working memory processing. The second component, the recurrent self-organising map memory, develops a temporal-distributed representation of speech using phone-like structures. Representing speech in terms of phonetic features in an emergent self-organised fashion, according to research on child cognitive development, recreates the approach found in infants. Using this representational approach, in a fashion similar to infants, should improve the performance of automatic recognition systems through aiding speech segmentation and fast word learning.
Original languageEnglish
Pages (from-to)157-175
Number of pages19
JournalConnection Science
Volume22
Issue number2
Early online date18 May 2010
DOIs
Publication statusE-pub ahead of print - 18 May 2010
Externally publishedYes

Keywords

  • working memory
  • emergent speech representation
  • attention-gating reinforcement mechanism
  • recurrent self-organised map learning

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