Publications

Abstract

This corpus study investigated pronunciation variants of word-final obstruent-liquid-schwa (OLS) clusters in nouns in casual Parisian French. Results showed that at least one phoneme was absent in 80.7% of the 291 noun tokens in the dataset, and that the whole cluster was absent (e.g., [mis] for ministre) in no less than 15.5% of the tokens. We demonstrate that phonemes are not always completely absent, but that they may leave traces on neighbouring phonemes. Further, the clusters display undocumented voice assimilation patterns. Statistical modelling showed that a phoneme is most likely to be absent if the following phoneme is also absent. The durations of the phonemes are conditioned particularly by the position of the word in the prosodic phrase. We argue, on the basis of three different types of evidence, that in French word-final OLS clusters, the absence of obstruents is mainly due to gradient reduction processes, whereas the absence of schwa and liquids may also be due to categorical deletion processes.

Abstract

Perceptual learning of novel accents is a critical skill for second-language speech perception, but little is known about the mechanisms that facilitate perceptual learning in communicative contexts. To study perceptual learning in an interactive dialogue setting while maintaining experimental control of the phonetic input, we employed an innovative experimental method incorporating prerecorded speech into a naturalistic conversation. Using both computer-based and face-to-face dialogue settings, we investigated the effect of two types of learning mechanisms in interaction: explicit corrective feedback and implicit lexical guidance. Dutch participants played an information-gap game featuring minimal pairs with an accented English speaker whose /ε/ pronunciations were shifted to /ɪ/. Evidence for the vowel shift came either from corrective feedback about participants’ perceptual mistakes or from onscreen lexical information that constrained their interpretation of the interlocutor’s words. Corrective feedback explicitly contrasting the minimal pairs was more effective than generic feedback. Additionally, both receiving lexical guidance and exhibiting more uptake for the vowel shift improved listeners’ subsequent online processing of accented words. Comparable learning effects were found in both the computer-based and face-to-face interactions, showing that our results can be generalized to a more naturalistic learning context than traditional computer-based perception training programs.

Abstract

This study addresses the question whether visually grounded speech recognition (VGS) models learn to capture sentence semantics without access to any prior linguistic knowledge. We produce synthetic and natural spoken versions of a well known semantic textual similarity database and show that our VGS model produces embeddings that correlate well with human semantic similarity judgements. Our results show that a model trained on a small image-caption database outperforms two models trained on much larger databases, indicating that database size is not all that matters. We also investigate the importance of having multiple captions per image and find that this is indeed helpful even if the total number of images is lower, suggesting that paraphrasing is a valuable learning signal. While the general trend in the field is to create ever larger datasets to train models on, our findings indicate other characteristics of the database can just as important.

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 article addresses the recognition of reduced word forms, which are frequent in casual speech. We describe two experiments on Dutch showing that listeners only recognize highly reduced forms well when these forms are presented in their full context and that the probability that a listener recognizes a word form in limited context is strongly correlated with the degree of reduction of the form. Moreover, we show that the effect of degree of reduction can only partly be interpreted as the effect of the intelligibility of the acoustic signal, which is negatively correlated with degree of reduction. We discuss the consequences of our findings for models of spoken word recognition and especially for the role that storage plays in these models.