Publications

Abstract

We propose that word recognition in continuous speech is subject to constraints on what may constitute a viable word of the language. This Possible-Word Constraint (PWC) reduces activation of candidate words if their recognition would imply word status for adjacent input which could not be a word - for instance, a single consonant. In two word-spotting experiments, listeners found it much harder to detectapple,for example, infapple(where [f] alone would be an impossible word), than invuffapple(wherevuffcould be a word of English). We demonstrate that the PWC can readily be implemented in a competition-based model of continuous speech recognition, as a constraint on the process of competition between candidate words; where a stretch of speech between a candidate word and a (known or likely) word boundary is not a possible word, activation of the candidate word is reduced. This implementation accurately simulates both the present results and data from a range of earlier studies of speech segmentation.

Abstract

Finnish vowel harmony rules require that if the vowel in the first syllable of a word belongs to one of two vowel sets, then all subsequent vowels in that word must belong either to the same set or to a neutral set. A harmony mismatch between two syllables containing vowels from the opposing sets thus signals a likely word boundary. We report five experiments showing that Finnish listeners can exploit this information in an on-line speech segmentation task. Listeners found it easier to detect words likehymyat the end of the nonsense stringpuhymy(where there is a harmony mismatch between the first two syllables) than in the stringpyhymy(where there is no mismatch). There was no such effect, however, when the target words appeared at the beginning of the nonsense string (e.g.,hymypuvshymypy). Stronger harmony effects were found for targets containing front harmony vowels (e.g.,hymy) than for targets containing back harmony vowels (e.g.,paloinkypaloandkupalo). The same pattern of results appeared whether target position within the string was predictable or unpredictable. Harmony mismatch thus appears to provide a useful segmentation cue for the detection of word onsets in Finnish speech.

Abstract

Several models of spoken word recognition postulate that recognition is achieved via a process of competition between lexical hypotheses. Competition not only provides a mechanism for isolated word recognition, it also assists in continuous speech recognition, since it offers a means of segmenting continuous input into individual words. We present statistics on the pattern of occurrence of words embedded in the polysyllabic words of the English vocabulary, showing that an overwhelming majority (84%) of polysyllables have shorter words embedded within them. Positional analyses show that these embeddings are most common at the onsets of the longer word. Although both phonological and syntactic constraints could rule out some embedded words, they do not remove the problem. Lexical competition provides a means of dealing with lexical embedding. It is also supported by a growing body of experimental evidence. We present results which indicate that competition operates both between word candidates that begin at the same point in the input and candidates that begin at different points (McQueen, Norris, & Cutler, 1994, Noms, McQueen, & Cutler, in press). We conclude that lexical competition is an essential component in models of continuous speech recognition.

Abstract

Spoken utterances contain few reliable cues to word boundaries, but listeners nonetheless experience little difficulty identifying words in continuous speech. The authors present data and simulations that suggest that this ability is best accounted for by a model of spoken-word recognition combining competition between alternative lexical candidates and sensitivity to prosodic structure. In a word-spotting experiment, stress pattern effects emerged most clearly when there were many competing lexical candidates for part of the input. Thus, competition between simultaneously active word candidates can modulate the size of prosodic effects, which suggests that spoken-word recognition must be sensitive both to prosodic structure and to the effects of competition. A version of the Shortlist model ( D. G. Norris, 1994b) incorporating the Metrical Segmentation Strategy ( A. Cutler & D. Norris, 1988) accurately simulates the results using a lexicon of more than 25,000 words.

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.