Publications

Abstract

All languages instantiate a consonant/vowel contrast. This contrast has processing consequences at different levels of spoken-language recognition throughout the lifespan. In adulthood, lexical processing is more strongly associated with consonant than with vowel processing; this has been demonstrated across 13 languages from seven language families and in a variety of auditory lexical-level tasks (deciding whether a spoken input is a word, spotting a real word embedded in a minimal context, reconstructing a word minimally altered into a pseudoword, learning new words or the “words” of a made-up language), as well as in written-word tasks involving phonological processing. In infancy, a consonant advantage in word learning and recognition is found to emerge during development in some languages, though possibly not in others, revealing that the stronger lexicon–consonant association found in adulthood is learned. Current research is evaluating the relative contribution of the early acquisition of the acoustic/phonetic and lexical properties of the native language in the emergence of this association

Abstract

Not only can the pitfalls that Firestone & Scholl (F&S) identify be generalised across multiple studies within the field of visual perception, but also they have general application outside the field wherever perceptual and cognitive processing are compared. We call attention to the widespread susceptibility of research on the perception of speech to versions of the same pitfalls.

Abstract

Speech perception involves prediction, but how is that prediction implemented? In cognitive models prediction has often been taken to imply that there is feedback of activation from lexical to pre-lexical processes as implemented in interactive-activation models (IAMs). We show that simple activation feedback does not actually improve speech recognition. However, other forms of feedback can be beneficial. In particular, feedback can enable the listener to adapt to changing input, and can potentially help the listener to recognise unusual input, or recognise speech in the presence of competing sounds. The common feature of these helpful forms of feedback is that they are all ways of optimising the performance of speech recognition using Bayesian inference. That is, listeners make predictions about speech because speech recognition is optimal in the sense captured in Bayesian models.

Abstract

The recognition of speech involves the segmentation of continuous utterances into their component words. Cross-linguistic evidence is briefly reviewed which suggests that although there are language-specific solutions to this segmentation problem, they have one thing in common: they are all based on language rhythm. In English, segmentation is stress-based: strong syllables are postulated to be the onsets of words. Segmentation, however, can also be achieved by a process of competition between activated lexical hypotheses, as in the Shortlist model. A series of experiments is summarised showing that segmentation of continuous speech depends on both lexical competition and a metrically-guided procedure. In the final section, the implementation of metrical segmentation in the Shortlist model is described: the activation of lexical hypotheses matching strong syllables in the input is boosted and that of hypotheses mismatching strong syllables in the input is penalised.

Abstract

Japanese listeners detect speech sound targets which correspond precisely to a mora (a phonological unit which is the unit of rhythm in Japanese) more easily than targets which do not. English listeners detect medial vowel targets more slowly than consonants. Six phoneme detection experiments investigated these effects in both subject populations, presented with native- and foreign-language input. Japanese listeners produced faster and more accurate responses to moraic than to nonmoraic targets both in Japanese and, where possible, in English; English listeners responded differently. The detection disadvantage for medial vowels appeared with English listeners both in English and in Japanese; again, Japanese listeners responded differently. Some processing operations which listeners apply to speech input are language-specific; these language-specific procedures, appropriate for listening to input in the native language, may be applied to foreign-language input irrespective of whether they remain appropriate.

Abstract

Although word boundaries are rarely clearly marked, listeners can rapidly recognize the individual words of spoken sentences. Some theories explain this in terms of competition between multiply activated lexical hypotheses; others invoke sensitivity to prosodic structure. We describe a connectionist model, SHORTLIST, in which recognition by activation and competition is successful with a realistically sized lexicon. Three experiments are then reported in which listeners detected real words embedded in nonsense strings, some of which were themselves the onsets of longer words. Effects both of competition between words and of prosodic structure were observed, suggesting that activation and competition alone are not sufficient to explain word recognition in continuous speech. However, the results can be accounted for by a version of SHORTLIST that is sensitive to prosodic structure.

Abstract

The order of some conjoined words is rigidly fixed (e.g. dribs and drabs/*drabs and dribs). Both phonetic and semantic factors can play a role in determining the fixed order. An experiment was conducted to test whether listerners’ reaction times for monitoring a predetermined phoneme are influenced by phonetic constraints on ordering. Two such constraints were investigated: monosyllable-bissyllable and high-low vowel sequences. In English, conjoined words occur in such sequences with much greater frequency than their converses, other factors being equal. Reaction times were significantly shorter for phoneme monitoring in monosyllable-bisyllable sequences than in bisyllable- monosyllable sequences. However, reaction times were not significantly different for high-low vs. low-high vowel sequences.