Three word-spotting experiments assessed the role of syllable onsets and offsets in lexical segmentation. Participants detected CVC words embedded initially or finally in bisyllabic nonwords with aligned (CVC.CVC) or misaligned (CV.CCVC) syllabic structure. A misalignment between word and syllable onsets (Experiment 1) produced a greater perceptual cost than a misalignment between word and syllable offsets (Experiments 2 and 3). These results suggest that listeners rely on syllable onsets to locate the beginning of words. The implications for theories of lexical access in continuous speech are discussed.

The lack of acoustic markers of word boundaries in connected speech may create temporary ambiguities between words like cap and the start of longer words like captain. These ambiguities have motivated models of spoken word recognition in which lexical competition allows information after the end of an embedded word to assist identification. We review the results of priming experiments demonstrating that additional acoustic cues assist listeners in distinguishing embedded words from longer competitors. We present a recurrent network model in which acoustic information and following context combine in the identification of onset-embedded words. Simulations show an activation profile consistent with the cross-modal priming data suggesting that recurrent networks can be used to model the identification of onset-embedded words. 1. INTRODUCTION Connected speech contains few reliable acoustic markers of word boundaries [1]. Although non-lexical cues or strategies may allow segmentation prio...

In this paper, we illustrate the close parallels between the research fields of human speech recognition (HSR) and automatic speech recognition (ASR) using a computational model of human word recognition, SpeM, which was built using techniques from ASR. We show that ASR has proven to be useful for improving models of HSR by relieving them of some of their shortcomings. However, in order to build an integrated computational model of all aspects of HSR, a lot of issues remain to be resolved. In this process, ASR algorithms and techniques definitely can play an important role. 1.

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.

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this paper derive from the discussions held in Sperlonga at the "Cognitive Models of Speech Processing" conference

Language www.elsevier.com/locate/jml Lexical competition in non-native spoken-word recognition

lexical embedding in speech comprehension

coMMuNlcAT The comparative perspective on spoken-language processing