Lexical Segmentation in Spoken Word Recognition
Matt. H. Davis (2000)
PhD Thesis, Psychology Department, Birkbeck College.
Abstract:This thesis examines an important issue in spoken word recognition; how the perceptual system segments connected speech into lexical units or words. Research on this topic has investigated the role of different sources of information in dividing up the speech stream: acoustic cues in the speech signal, statistical regularities in the structure of the language or through the identification of individual lexical items.
This research focuses on cases in which the location of word boundaries may be ambiguous by one or more of these segmentation mechanisms using words embedded at the onset of longer words (such as cap in captain). The ambiguities proposed for onset-embedded words have motivated accounts of segmentation based on competition between alternative parses of speech into words. In these accounts, the recognition of embedded words is delayed until after their offset when subsequent input rules out longer competitors. In this thesis it is demonstrated that training a simple recurrent network to activate a representation of all the words in a sequence allows a connectionist network to learn the appropriate delay to allow the identification of onset-embedded words without requiring directly implemented competition between words.
Both lexical competition and recurrent network models assume ambiguity between onset-embedded words and equivalent syllables in longer competitors. Acoustic analysis carried out in this thesis confirms the presence of reliable acoustic differences between syllables in short and long words. A series of experiments using gating and cross-modal priming suggest that the perceptual system uses these acoustic differences to discriminate embedded words from the onset of longer competitors and that match or mismatch with longer competitors may be less important for the identification of onset-embedded words. These results are interpreted within a revised version of the recurrent network model, incorporating input representing the acoustic differences between syllables in short and long words.
Download entire thesis in pdf format here (240 pages, 1.5 spaced, A4 paper)
Or you can download single chapters:
Chapter 0 Title, Abstract, Table
of Contents, Table of Figures etc.
Chapter 1 Introduction
Chapter 2 Segmentation in lexical access
Chapter 3 Connectionist models of spoken word recognition
Chapter 4 Investigating the recognition of embedded words
Chapter 5 Cross-modal priming experiments with embedded words
Chapter 6 Acoustic cues to word length in recurrent networks
Chapter 7 Effects of following context in recognising embedded words
Chapter 8 Concluding remarks
with thanks to Calvin and Hobbes