Publications

Abstract

Do individuals differ in how efficiently they process non-native sounds? To what extent do these differences relate to individual variability in sound-learning aptitude? We addressed these questions by assessing the sound-learning abilities of Dutch native speakers as they were trained on non-native tone contrasts. We used fMRI repetition suppression to the non-native tones to measure participants' neuronal processing efficiency before and after training. Although all participants improved in tone identification with training, there was large individual variability in learning performance. A repetition suppression effect to tone was found in the bilateral inferior frontal gyri (IFGs) before training. No whole-brain effect was found after training; a region-of-interest analysis, however, showed that, after training, repetition suppression to tone in the left IFG correlated positively with learning. That is, individuals who were better in learning the non-native tones showed larger repetition suppression in this area. Crucially, this was true even before training. These findings add to existing evidence that the left IFG plays an important role in sound learning and indicate that individual differences in learning aptitude stem from differences in the neuronal efficiency with which non-native sounds are processed.

Abstract

This commentary on Gow and Olson [2015. Sentential influences on acoustic-phonetic processing: A Granger causality analysis of multimodal imaging data. Language, Cognition and Neuroscience. doi:10.1080/23273798.2015.1029498] questions in three ways their conclusion that speech perception is based on interactive processing. First, it is not clear that the data presented by Gow and Olson reflect normal speech recognition. Second, Gow and Olson's conclusion depends on still-debated assumptions about the functions performed by specific brain regions. Third, the results are compatible with feedforward models of speech perception and appear inconsistent with models in which there are online interactions about phonological content. We suggest that progress in the neuroscience of speech perception requires the generation of testable hypotheses about the function(s) performed by inter-regional connections

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

Clashen proposes two distinct processing routes, for regularly and irregularly inflected forms, respectively, and thus is apparently making a psychological claim. We argue his position, which embodies a strictly linguistic perspective, does not constitute a psychological processing model.

Abstract

In 5 experiments, listeners heard words and nonwords, some cross-spliced so that they contained acoustic-phonetic mismatches. Performance was worse on mismatching than on matching items. Words cross-spliced with words and words cross-spliced with nonwords produced parallel results. However, in lexical decision and 1 of 3 phonetic decision experiments, performance on nonwords cross-spliced with words was poorer than on nonwords cross-spliced with nonwords. A gating study confirmed that there were misleading coarticulatory cues in the cross-spliced items; a sixth experiment showed that the earlier results were not due to interitem differences in the strength of these cues. Three models of phonetic decision making (the Race model, the TRACE model, and a postlexical model) did not explain the data. A new bottom-up model is outlined that accounts for the findings in terms of lexical involvement at a dedicated decision-making stage.

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

In a 50,000-word corpus of spoken British English the occurrence of words embedded within other words is reported. Within-word embedding in this real speech sample is common, and analogous to the extent of embedding observed in the vocabulary. Imposition of a syllable boundary matching constraint reduces but by no means eliminates spurious embedding. Embedded words are most likely to overlap with the beginning of matrix words, and thus may pose serious problems for speech recognisers.

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

This paper describes recent experimental evidence which shows that models of spoken word recognition must incorporate both inhibition between competing lexical candidates and a sensitivity to metrical cues to lexical segmentation. A new version of the Shortlist [1][2] model incorporating the Metrical Segmentation Strategy [3] provides a detailed simulation of the data.