
Contents
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4.1 Introduction 4.1 Introduction
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4.2 Modeling styles 4.2 Modeling styles
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4.2.1 Interactive activation and competition (IAC) 4.2.1 Interactive activation and competition (IAC)
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4.2.2 Error-driven learning networks 4.2.2 Error-driven learning networks
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4.2.3 Statistical models 4.2.3 Statistical models
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4.2.4 Adaptive resonance theory (ART) 4.2.4 Adaptive resonance theory (ART)
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4.3 Prelexical representations of speech 4.3 Prelexical representations of speech
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4.3.1 Representation and dynamics in pre-lexical processing 4.3.1 Representation and dynamics in pre-lexical processing
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4.3.2 Emergence of phonological representations 4.3.2 Emergence of phonological representations
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4.3.3 Evolution of phonological representations 4.3.3 Evolution of phonological representations
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4.4 Word segmentation 4.4 Word segmentation
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4.4.1 Bottom-up approaches 4.4.1 Bottom-up approaches
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4.4.2 Lexical approaches 4.4.2 Lexical approaches
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4.5 Lexical representation and competition 4.5 Lexical representation and competition
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4.5.1 Granularity of representation 4.5.1 Granularity of representation
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4.5.2 Dynamics of lexical competition 4.5.2 Dynamics of lexical competition
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4.6 Conclusion 4.6 Conclusion
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Acknowledgements Acknowledgements
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References References
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4 Statistical and connectionist models of speech perception and word recognition
Get accessM. Gareth Gaskell, University of York, UK
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Published:18 September 2012
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Abstract
This article reviews the current impact of connectionism in the area of speech perception and spoken word recognition. A major advance that connectionism provided was to highlight the value and power of statistical models of language processing. Therefore, some types of statistical model—particularly those stressing statistical learning—are reviewed alongside connectionist theories such as interactive activation and competition models, error-driven learning networks, and adaptive resonance theory. The article examines how connectionist models represent speech pre-lexically, and how such prelexical representations might develop and adapt to fit the requirements of the perceptual system. It also looks at the process of word segmentation, again addressing both acquisition issues and the degree to which connectionist models can explain performance in the adult system. Finally, the article considers the process of word recognition, as modeled in terms of lexical competition. Key issues here include whether distributed models can cope with the specific properties that are imposed by the speech medium, such as the drawn-out nature of the input and the consequent requirement to entertain multiple hypothesis (parallel activation) during recognition.
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