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WIREs Cogn Sci
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Models of spoken‐word recognition

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Abstract All words of the languages we know are stored in the mental lexicon. Psycholinguistic models describe in which format lexical knowledge is stored and how it is accessed when needed for language use. The present article summarizes key findings in spoken‐word recognition by humans and describes how models of spoken‐word recognition account for them. Although current models of spoken‐word recognition differ considerably in the details of implementation, there is general consensus among them on at least three aspects: multiple word candidates are activated in parallel as a word is being heard, activation of word candidates varies with the degree of match between the speech signal and stored lexical representations, and activated candidate words compete for recognition. No consensus has been reached on other aspects such as the flow of information between different processing levels, and the format of stored prelexical and lexical representations. WIREs Cogn Sci 2012, 3:387–401. doi: 10.1002/wcs.1178 This article is categorized under: Linguistics > Computational Models of Language

Waveform and spectrogram of the utterance “The sun began to rise”. The horizontal axis represents time and the vertical axis amplitude for the waveform and frequency for the spectrogram, with greater energy being represented by darker shading. White spaces in the spectrogram correspond to breaks in the speech signal. The vertical dotted lines are aligned as closely as possible with word boundaries.

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Recognition process of the word sun by Fine‐Tracker. The acoustic signal is transformed into a sequence of feature vectors over time by a set of artificial neural networks. At the word layer, words are represented as feature vectors, for better visualisation they are depicted as phonemes in the figure. Fine‐Tracker's lexicon is implemented as a lexical tree, with ‘B’ as the beginning of the tree. Not all possible paths in the lexical tree are shown. Each node can be followed by multiple other nodes, indicated with the dotted arrows as examples. The input feature vectors and the lexical feature vectors are mapped onto one another using a probabilistic word search.

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Recognition process of the word sun by Shortlist. For every time slice, a new shortlist (indicated with the gray box) is created, which is subsequently wired into the competition stage. For better visualisation this repetition is not depicted in the figure. Candidate words that overlap with each other at any position compete with one another. In this example, all candidate words would inhibit one another; however, for better visualisation not all inhibitory connections are shown.

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Recognition process of the word sun by TRACE. For every time slice the entire network is copied, for better visualisation this duplication is not depicted in the figure. Activation in the lower layers flows upwards to the higher levels to all nodes that incorporate the lower layer node. Activation from the word layer also flows back to the phoneme layer.

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