Publications

Abstract

That speakers can vary their speaking rate is evident, but how they accomplish this has hardly been studied. Consider this analogy: When walking, speed can be continuously increased, within limits, but to speed up further, humans must run. Are there multiple qualitatively distinct speech “gaits” that resemble walking and running? Or is control achieved by continuous modulation of a single gait? This study investigates these possibilities through simulations of a new connectionist computational model of the cognitive process of speech production, EPONA, that borrows from Dell, Burger, and Svec’s (1997) model. The model has parameters that can be adjusted to fit the temporal characteristics of speech at different speaking rates. We trained the model on a corpus of disyllabic Dutch words produced at different speaking rates. During training, different clusters of parameter values (regimes) were identified for different speaking rates. In a 1-gait system, the regimes used to achieve fast and slow speech are qualitatively similar, but quantitatively different. In a multiple gait system, there is no linear relationship between the parameter settings associated with each gait, resulting in an abrupt shift in parameter values to move from speaking slowly to speaking fast. After training, the model achieved good fits in all three speaking rates. The parameter settings associated with each speaking rate were not linearly related, suggesting the presence of cognitive gaits. Thus, we provide the first computationally explicit account of the ability to modulate the speech production system to achieve different speaking styles.

Abstract

This paper discusses four experiments on Dutch which show that distinctive phonological features differ in their relevance for word recognition. The relevance of a feature for word recognition depends on its phonological stability, that is, the extent to which that feature is generally realized in accordance with its lexical specification in the relevant word position. If one feature value is uninformative, all values of that feature are less relevant for word recognition, with the least informative feature being the least relevant. Features differ in their relevance both in spoken and written word recognition, though the differences are more pronounced in auditory lexical decision than in self-paced reading.

Abstract

This study addresses the question to what extent the production of regular past tense forms in Dutch is a¤ected by analogical processes. We report an experiment in which native speakers of Dutch listened to existing regular verbs over headphones, and had to indicate which of the past tense allomorphs, te or de, was appropriate for these verbs. According to generative analyses, the choice between the two su‰xes is completely regular and governed by the underlying [voice]-specification of the stem-final segment. In this approach, no analogical e¤ects are expected. In connectionist and analogical approaches, by contrast, the phonological similarity structure in the lexicon is expected to a¤ect lexical processing. Our experimental results support the latter approach: all participants created more nonstandard past tense forms, produced more inconsistency errors, and responded more slowly for verbs with stronger analogical support for the nonstandard form.

Abstract

Listeners cannot recognize highly reduced word forms in isolation, but they can do so when these forms are presented in context (Ernestus, Baayen, & Schreuder, 2002). This suggests that not all possible surface forms of words have equal status in the mental lexicon. The present study shows that the reduced forms are linked to the canonical representations in the mental lexicon, and that these latter representations induce reconstruction processes. Listeners restore suffixes that are partly or completely missing in reduced word forms. A series of phoneme-monitoring experiments reveals the nature of this restoration: the basis for suffix restoration is mainly phonological in nature, but orthography has an influence as well.

Abstract

In this paper, we show that both token and type-based effects in lexical processing can result from a single, token-based, system, and therefore, do not necessarily reflect different levels of processing. We report three Simple Recurrent Networks modeling Dutch past-tense formation. These networks show token-based frequency effects and type-based analogical effects closely matching the behavior of human participants when producing past-tense forms for both existing verbs and pseudo-verbs. The third network covers the full vocabulary of Dutch, without imposing predefined linguistic structure on the input or output words.

Abstract

This paper discusses some pitfalls in corpus research and suggests solutions on the basis of examples and computer simulations. We first address reliability problems in language transcriptions, agreement between transcribers, and how disagreements can be dealt with. We then show that the frequencies of occurrence obtained from a corpus cannot always be analyzed with the traditional X2 test, as corpus data are often not sequentially independent and unit independent. Next, we stress the relevance of the power of statistical tests, and the sizes of statistically significant effects. Finally, we point out that a t-test based on log odds often provides a better alternative to a X2 analysis based on frequency counts.

Abstract

Among the most fascinating data for phonology are those showing how speakers incorporate new words and foreign words into their language system, since these data provide cues to the actual principles underlying language. In this article, we address how speakers deal with neutralized obstruents in new words. We formulate four hypotheses and test them on the basis of Dutch word-final obstruents, which are neutral for [voice]. Our experiments show that speakers predict the characteristics ofneutralized segments on the basis ofphonologically similar morphemes stored in the mental lexicon. This effect of the similar morphemes can be modeled in several ways. We compare five models, among them STOCHASTIC OPTIMALITY THEORY and ANALOGICAL MODELING OF LANGUAGE; all perform approximately equally well, but they differ in their complexity, with analogical modeling oflanguage providing the most economical explanation.