Publications

Abstract

The results of a study on perceptual learning are reported. Dutch subjects made lexical decisions on a list of words and nonwords. Embedded in the list were either [f]- or [s]-final words in which the final fricative had been replaced by an ambiguous sound, midway between [f] and [s]. One group of listeners heard ambiguous [f]- final Dutch words like [kara?] (based on karaf, carafe) and unambiguous [s]-final words (e.g., karkas, carcase). A second group heard the reverse (e.g., ambiguous [karka?] and unambiguous karaf). After this training phase, listeners labelled ambiguous fricatives on an [f]- [s] continuum. The subjects who had heard [?] in [f]- final words categorised these fricatives as [f] reliably more often than those who had heard [?] in [s]-final words. These results suggest that speech recognition is dynamic: the system adjusts to the constraints of each particular listening situation. The lexicon can provide this adjustment process with a training signal.

Abstract

We introduce the papers in this special issue by summarising the current major issues in spoken word recognition. We argue that a full understanding of the process of lexical access during speech comprehension will depend on resolving several key representational issues: what is the form of the representations used for lexical access; how is phonological information coded in the mental lexicon; and how is the morphological and semantic information about each word stored? We then discuss a number of distinct access processes: competition between lexical hypotheses; the computation of goodness-of-fit between the signal and stored lexical knowledge; segmentation of continuous speech; whether the lexicon influences prelexical processing through feedback; and the relationship of form-based processing to the processes responsible for deriving an interpretation of a complete utterance. We conclude that further progress may well be made by swapping ideas among the different sub-domains of the discipline.

Abstract

In two word-spotting experiments, Japanese listeners detected Japanese words faster in vowel contexts (e.g., agura, to sit cross-legged, in oagura) than in consonant contexts (e.g., tagura). In the same experiments, however, listeners spotted words in vowel contexts (e.g., saru, monkey, in sarua) no faster than in moraic nasal contexts (e.g., saruN). In a third word-spotting experiment, words like uni, sea urchin, followed contexts consisting of a consonant-consonant-vowel mora (e.g., gya) plus either a moraic nasal (gyaNuni), a vowel (gyaouni) or a consonant (gyabuni). Listeners spotted words as easily in the first as in the second context (where in each case the target words were aligned with mora boundaries), but found it almost impossible to spot words in the third (where there was a single consonant, such as the [b] in gyabuni, between the beginning of the word and the nearest preceding mora boundary). Three control experiments confirmed that these effects reflected the relative ease of segmentation of the words from their contexts.We argue that the listeners showed sensitivity to the viability of sound sequences as possible Japanese words in the way that they parsed the speech into words. Since single consonants are not possible Japanese words, the listeners avoided lexical parses including single consonants and thus had difficulty recognizing words in the consonant contexts. Even though moraic nasals are also impossible words, they were not difficult segmentation contexts because, as with the vowel contexts, the mora boundaries between the contexts and the target words signaled likely word boundaries. Moraic rhythm appears to provide Japanese listeners with important segmentation cues.

Abstract

Two word-spotting experiments are reported that examine whether the Possible-Word Constraint (PWC) is a language-specific or language-universal strategy for the segmentation of continuous speech. The PWC disfavours parses which leave an impossible residue between the end of a candidate word and any likely location of a word boundary, as cued in the speech signal. The experiments examined cases where the residue was either a CVC syllable with a schwa, or a CV syllable with a lax vowel. Although neither of these syllable contexts is a possible lexical word in English, word-spotting in both contexts was easier than in a context consisting of a single consonant. Two control lexical-decision experiments showed that the word-spotting results reflected the relative segmentation difficulty of the words in different contexts. The PWC appears to be language-universal rather than language-specific.

Abstract

Sentential context effects on the identification of the Dutch function words te (to) and de (the) were examined. In Experiment 1, listeners labeled words on a [tә]-[dә] continuum more often as te when the context was te biased (Ik probeer [?ә] schieten [I try to/the shoot]) than when it was de biased (Ik probeer [?ә] schoenen [I try to/the shoes]). The effect was weaker in slower responses. In Experiment 2, disambiguation began later, in the second word after [?ә]. There was a weak context effect only in the slower responses. In Experiments 3 and 4, disambiguation occurred on the word before [?ә]: There was no context effect when one set of sentences was used, but there was an effect (larger in the faster responses) when more sentences were used. Syntactic processing affects word identification only within a limited time frame. It appears to do so not by influencing lexical access processes through feedback but, instead, by biasing decision making.

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.