Publications

Abstract

Voice assimilation in Dutch is an optional phonological rule which changes the surface forms of words and in doing so may violate the otherwise obligatory phonological rule of syllablefinal devoicing. We report two experiments examining the influence of voice assimilation on phoneme processing, in lexical compound words and in noun-verb phrases. Processing was not impaired in appropriate assimilation contexts across morpheme boundaries, but was impaired when devoicing was violated (a) in an inappropriate non-assimilatory) context, or (b) across a syntactic boundary.

Abstract

Citations play a crucial role in the scientific discourse, in information retrieval, and in bibliometrics. Many initiatives are currently promoting the idea of having free and open citation data. Creation of citation data, however, is not part of the cataloging workflow in libraries nowadays.
In this paper, we present our project Linked Open Citation Database, in which we design distributed processes and a system infrastructure based on linked data technology. The goal is to show that efficiently cataloging citations in libraries using a semi-automatic approach is possible. We specifically describe the current state of the workflow and its implementation. We show that we could significantly improve the automatic reference extraction that is crucial for the subsequent data curation. We further give insights on the curation and linking process and provide evaluation results that not only direct the further development of the project, but also allow us to discuss its overall feasibility.

Abstract

It is widely held that smells and flavors are impossible to put into words. In this paper we test this claim by seeking predictive patterns in wine reviews, which ostensibly aim to provide guides to perceptual content. Wine reviews have previously been critiqued as random and meaningless. We collected an English corpus of wine reviews with their structured metadata, and applied machine learning techniques to automatically predict the wine's color, grape variety, and country of origin. To train the three supervised classifiers, three different information sources were incorporated: lexical bag-of-words features, domain-specific terminology features, and semantic word embedding features. In addition, using regression analysis we investigated basic review properties, i.e., review length, average word length, and their relationship to the scalar values of price and review score. Our results show that wine experts do share a common vocabulary to describe wines and they use this in a consistent way, which makes it possible to automatically predict wine characteristics based on the review text alone. This means that odors and flavors may be more expressible in language than typically acknowledged.

Abstract

Annotations of media recordings are the grounds for linguistic research. Since creating those annotations is a very laborious task, reaching 100 times longer than the length of the annotated media, innovative audio and video processing algorithms are needed, in order to improve the efficiency and quality of annotation process. The AVATecH project, started by the Max-Planck Institute for Psycholinguistics (MPI) and the Fraunhofer institutes HHI and IAIS, aims at significantly speeding up the process of creating annotations of audio-visual data for humanities research. In order for this to be achieved a range of state-of-the-art audio and video pattern recognition algorithms have been developed and integrated into widely used ELAN annotation tool. To address the problem of heterogeneous annotation tasks and recordings we provide modular components extended by adaptation and feedback mechanisms to achieve competitive annotation quality within significantly less annotation time.

Abstract

Evolution and changes of all modern languages is a well-known fact. However, recently it is reaching dynamics never seen before, which results in loss of the vast amount of information encoded in every language. In order to preserve such heritage, properly annotated recordings of world languages are necessary. Since creating those annotations is a very laborious task, reaching times 100 longer than the length of the annotated media, innovative video processing algorithms are needed, in order to improve the efficiency and quality of annotation process.

Abstract

Processing medical data has always been an interesting field that has shown the need for effective image segmentation methods. Modern medical image segmentation solutions are focused on 3D image volumes, which originate at advanced acquisition devices. Operating on such data in a 3D envi- ronment is essential in order to take the full advantage of the available information. In this paper we present an extended version of our 3D image segmentation and reconstruction model that belongs to the family of Deformable Models and is capable of processing large image volumes in competitive times and in fully 3D environment, offering a big level of automation of the process and a high precision of results. It is also capable of handling topology changes and offers a very good scalability on multi-processing unit architectures. We present a description of the model and show its capabilities in the field of medical image processing.

Abstract

Recent work has investigated the emergence of structure in speech using experiments which use artificial continuous signals. Some experiments have had no limit on the duration which signals can have (e.g. Verhoef et al., 2014), and others have had time limitations (e.g. Verhoef et al., 2015). However, the effect of time constraints on the structure in signals has never been experimentally investigated.

Abstract

Structure of language is not only caused by cognitive processes, but also by physical aspects of the signalling modality. We test the assumptions surrounding the role which the physical aspects of the signal space will have on the emergence of structure in speech. Here, we use a signal creation task to test whether a signal space and a meaning space having similar dimensionalities will generate an iconic system with signal-meaning mapping and whether, when the topologies differ, the emergence of non-iconic structure is facilitated. In our experiments, signals are created using infrared sensors which use hand position to create audio signals. We find that people take advantage of signal-meaning mappings where possible. Further, we use trajectory probabilities and measures of variance to show that when there is a dimensionality mismatch, more structural strategies are used.

Abstract

Studies of sound symbolism have shown that people can associate sound and meaning in consistent ways when presented with maximally contrastive stimulus pairs of nonwords such as bouba/kiki (rounded/sharp) or mil/mal (small/big). Recent work has shown the effect extends to antonymic words from natural languages and has proposed a role for shared cross-modal correspondences in biasing form-to-meaning associations. An important open question is how the associations work, and particularly what the role is of sound-symbolic matches versus mismatches. We report on a learning task designed to distinguish between three existing theories by using a spectrum of sound-symbolically matching, mismatching, and neutral (neither matching nor mismatching) stimuli. Synthesized stimuli allow us to control for prosody, and the inclusion of a neutral condition allows a direct test of competing accounts. We find evidence for a sound-symbolic match boost, but not for a mismatch difficulty compared to the neutral condition.

Abstract

We present the Narrative Brain Dataset, an fMRI dataset that was collected during spoken presentation of short excerpts of three
stories in Dutch. Together with the brain imaging data, the dataset contains the written versions of the stimulation texts. The texts are
accompanied with stochastic (perplexity and entropy) and semantic computational linguistic measures. The richness and unconstrained
nature of the data allows the study of language processing in the brain in a more naturalistic setting than is common for fMRI studies.
We hope that by making NBD available we serve the double purpose of providing useful neural data to researchers interested in natural
language processing in the brain and to further stimulate data sharing in the field of neuroscience of language.

Abstract

Linguistic traits are often seen as reflecting cognitive biases and constraints (e.g. Christiansen & Chater, 2008). However, language must also adapt to properties of the channel through which communication between individuals occurs. Perhaps the most basic aspect of any communication channel is noise. Communicative signals can be blocked, degraded or distorted by other sources in the environment. This poses a fundamental problem for communication. On average, channel disruption accompanies problems in conversation every 3 minutes (27% of cases of other-initiated repair, Dingemanse et al., 2015). Linguistic signals must adapt to this harsh environment. While modern language structures are robust to noise (e.g. Piantadosi et al., 2011), we investigate how noise might have shaped the early emergence of structure in language. The obvious adaptation to noise is redundancy. Signals which are maximally different from competitors are harder to render ambiguous by noise. Redundancy can be increased by adding differentiating segments to each signal (increasing the diversity of segments). However, this makes each signal more complex and harder to learn. Under this strategy, holistic languages may emerge. Another strategy is reduplication - repeating parts of the signal so that noise is less likely to disrupt all of the crucial information. This strategy does not increase the difficulty of learning the language - there is only one extra rule which applies to all signals. Therefore, under pressures for learnability, expressivity and redundancy, reduplicated signals are expected to emerge. However, reduplication is not a pervasive feature of words (though it does occur in limited domains like plurals or iconic meanings). We suggest that this is due to the pressure for redundancy being lifted by conversational infrastructure for repair. Receivers can request that senders repeat signals only after a problem occurs. That is, robustness is achieved by repeating the signal across conversational turns (when needed) instead of within single utterances. As a proof of concept, we ran two iterated learning chains with pairs of individuals in generations learning and using an artificial language (e.g. Kirby et al., 2015). The meaning space was a structured collection of unfamiliar images (3 shapes x 2 textures x 2 outline types). The initial language for each chain was the same written, unstructured, fully expressive language. Signals produced in each generation formed the training language for the next generation. Within each generation, pairs played an interactive communication game. The director was given a target meaning to describe, and typed a word for the matcher, who guessed the target meaning from a set. With a 50% probability, a contiguous section of 3-5 characters in the typed word was replaced by ‘noise’ characters (#). In one chain, the matcher could initiate repair by requesting that the director type and send another signal. Parallel generations across chains were matched for the number of signals sent (if repair was initiated for a meaning, then it was presented twice in the parallel generation where repair was not possible) and noise (a signal for a given meaning which was affected by noise in one generation was affected by the same amount of noise in the parallel generation). For the final set of signals produced in each generation we measured the signal redundancy (the zip compressibility of the signals), the character diversity (entropy of the characters of the signals) and systematic structure (z-score of the correlation between signal edit distance and meaning hamming distance). In the condition without repair, redundancy increased with each generation (r=0.97, p=0.01), and the character diversity decreased (r=-0.99,p=0.001) which is consistent with reduplication, as shown below (part of the initial and the final language): Linear regressions revealed that generations with repair had higher overall systematic structure (main effect of condition, t = 2.5, p < 0.05), increasing character diversity (interaction between condition and generation, t = 3.9, p = 0.01) and redundancy increased at a slower rate (interaction between condition and generation, t = -2.5, p < 0.05). That is, the ability to repair counteracts the pressure from noise, and facilitates the emergence of compositional structure. Therefore, just as systems to repair damage to DNA replication are vital for the evolution of biological species (O’Brien, 2006), conversational repair may regulate replication of linguistic forms in the cultural evolution of language. Future studies should further investigate how evolving linguistic structure is shaped by interaction pressures, drawing on experimental methods and naturalistic studies of emerging languages, both spoken (e.g Botha, 2006; Roberge, 2008) and signed (e.g Senghas, Kita, & Ozyurek, 2004; Sandler et al., 2005).

Abstract

For (semi-)automated subject indexing systems in digital libraries, it is often more practical to use metadata such as the title of a publication instead of the full-text or the abstract. Therefore, it is desirable to have good text mining and text classification algorithms that operate well already on the title of a publication. So far, the classification performance on titles is not competitive with the performance on the full-texts if the same number of training samples is used for training. However, it is much easier to obtain title data in large quantities and to use it for training than full-text data. In this paper, we investigate the question how models obtained from training on increasing amounts of title training data compare to models from training on a constant number of full-texts. We evaluate this question on a large-scale dataset from the medical domain (PubMed) and from economics (EconBiz). In these datasets, the titles and annotations of millions of publications are available, and they outnumber the available full-texts by a factor of 20 and 15, respectively. To exploit these large amounts of data to their full potential, we develop three strong deep learning classifiers and evaluate their performance on the two datasets. The results are promising. On the EconBiz dataset, all three classifiers outperform their full-text counterparts by a large margin. The best title-based classifier outperforms the best full-text method by 9.4%. On the PubMed dataset, the best title-based method almost reaches the performance of the best full-text classifier, with a difference of only 2.9%.

Abstract

How are the senses structured by the languages we speak, the cultures we inhabit? To what extent is the encoding of perceptual experiences in languages a matter of how the mind/brain is ―wired-up‖ and to what extent is it a question of local cultural preoccupation? The ―Language of Perception‖ project tests the hypothesis that some perceptual domains may be more ―ineffable‖ – i.e. difficult or impossible to put into words – than others. While cognitive scientists have assumed that proximate senses (olfaction, taste, touch) are more ineffable than distal senses (vision, hearing), anthropologists have illustrated the exquisite variation and elaboration the senses achieve in different cultural milieus. The project is designed to test whether the proximate senses are universally ineffable – suggesting an architectural constraint on cognition – or whether they are just accidentally so in Indo-European languages, so expanding the role of cultural interests and preoccupations. To address this question, a standardized set of stimuli of color patches, geometric shapes, simple sounds, tactile textures, smells and tastes have been used to elicit descriptions from speakers of more than twenty languages—including three sign languages. The languages are typologically, genetically and geographically diverse, representing a wide-range of cultures. The communities sampled vary in subsistence modes (hunter-gatherer to industrial), ecological zones (rainforest jungle to desert), dwelling types (rural and urban), and various other parameters. We examine how codable the different sensory modalities are by comparing how consistent speakers are in how they describe the materials in each modality. Our current analyses suggest that taste may, in fact, be the most codable sensorial domain across languages. Moreover, we have identified exquisite elaboration in the olfactory domains in some cultural settings, contrary to some contemporary predictions within the cognitive sciences. These results suggest that differential codability may be at least partly the result of cultural preoccupation. This shows that the senses are not just physiological phenomena but are constructed through linguistic, cultural and social practices.

Abstract

To study concepts, cognitive scientists must first identify some. The prevailing assumption is that they are revealed by words such as triangle, table, and robin. But languages vary dramatically in how they carve up the world by name. Either ordinary concepts must be heavily language-dependent or names cannot be a direct route to concepts. We asked English, Dutch, Spanish, and Japanese speakers to name videos of human locomotion and judge their similarities. We investigated what name inventories and scaling solutions on name similarity and on physical similarity for the groups individually and together suggest about the underlying concepts. Aggregated naming and similarity solutions converged on results distinct from the answers suggested by the word inventories and scaling solutions of any single language. Words such as triangle, table, and robin can help identify the conceptual space of a domain, but they do not directly reveal units of knowledge usefully considered 'concepts'.

Abstract

How listeners understand spoken words despite massive variation in the speech signal is a central issue for linguistic theory. A recent focus on lexical frequency and specificity has proved fruitful in accounting for this phenomenon. Speech perception, though, is a multi-faceted process and likely incorporates a number of mechanisms to map a variable signal to meaning. We examine a well-established language use factor — lexical frequency — and how this factor is integrated with phonetic variability during the perception of accented speech. We show that an integrated perspective highlights a low-level perceptual mechanism that accounts for the perception of accented speech absent native contrasts, while shedding light on the use of interactive language factors in the perception of spoken words.

Abstract

The ultimate goal of our research is to improve an existing speech-based computational model of human speech recognition on the task of simulating the role of fine-grained phonetic information in human speech processing. As part of this work we are investigating articulatory feature classifiers that are able to create reliable and accurate transcriptions of the articulatory behaviour encoded in the acoustic speech signal. Articulatory feature (AF) modelling of speech has received a considerable amount of attention in automatic speech recognition research. Different approaches have been used to build AF classifiers, most notably multi-layer perceptrons. Recently, deep neural networks have been applied to the task of AF classification. This paper aims to improve AF classification by investigating two different approaches: 1) investigating the usefulness of a deep Convolutional neural network (CNN) for AF classification; 2) integrating the Mel filtering operation into the CNN architecture. The results showed a remarkable improvement in classification accuracy of the CNNs over state-of-the-art AF classification results for Dutch, most notably in the minority classes. Integrating the Mel filtering operation into the CNN architecture did not further improve classification performance.

Abstract

This study demonstrates how interaction – specifically negotiation and repair – facilitates the emergence, evolution, and conventionalization of a silent gesture communication system. In a modified iterated learning paradigm, partners communicated noun-verb meanings using only silent gesture. The need to disambiguate similar noun-verb pairs drove these "new" language users to develop a morphology that allowed for quicker processing, easier transmission, and improved accuracy. The specific morphological system that emerged came about through a process of negotiation within the dyad, namely by means of repair. By applying a discourse analytic approach to the use of repair in an experimental methodology for language evolution, we are able to determine not only if interaction facilitates the emergence and learnability of a new communication system, but also how interaction affects such a system

Abstract

Speakers tend to take over the articulatory habits of their interlocutors [e.g., Pardo, JASA (2006)]. This phonetic alignment could be the consequence of either a social mechanism or a direct and automatic link between speech perception and production. The latter assumes that social variables should have little influence on phonetic alignment. To test that participants were engaged in a "cloze task" (i.e., Stimulus: "In fantasy movies, silver bullets are used to kill ..." Response: "werewolves") with either one or four interlocutors. Given findings with the Asch-conformity paradigm in social psychology, multiple consistent speakers should exert a stronger force on the participant to align. To control the speech style of the interlocutors, their questions and answers were pre-recorded in either a formal or a casual speech style. The stimuli's speech style was then manipulated between participants and was consistent throughout the experiment for a given participant. Surprisingly, participants aligned less with the speech style if there were multiple interlocutors. This may reflect a "diffusion of responsibility:" Participants may find it more important to align when they interact with only one person than with a larger group.

Abstract

Analyzing EEG signals recorded while participants are listening to continuous speech with the purpose of testing linguistic hypotheses is complicated by the fact that the signals simultaneously reflect exogenous acoustic excitation and endogenous linguistic processing. This makes it difficult to trace subtle differences that occur in mid-sentence position. We apply an analysis based on multivariate temporal response functions to uncover subtle mid-sentence effects. This approach is based on a per-stimulus estimate of the response of the neural system to speech input. Analyzing EEG signals predicted on the basis of the response functions might then bring to light conditionspecific differences in the filtered signals. We validate this approach by means of an analysis of EEG signals recorded with isolated word stimuli. Then, we apply the validated method to the analysis of the responses to the same words in the middle of meaningful sentences.

Abstract

This paper describes the Glottolog/Langdoc project, an at- tempt to provide near-total bibliographical coverage of descriptive re- sources to the world's languages. Every reference is treated as a resource, as is every \languoid"[1]. References are linked to the languoids which they describe, and languoids are linked to the references described by them. Family relations between languoids are modeled in SKOS, as are relations across dierent classications of the same languages. This setup allows the representation of languoids as collections of references, render- ing the question of the denition of entities like `Scots', `West-Germanic' or `Indo-European' more empirical.

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.

Abstract

There is a long-standing assumption that gestural forms are geared by a set of modes of representation (acting, representing, drawing, moulding) with each technique expressing speakers’ focus of attention on specific aspects of referents (Müller, 2013). Beyond different taxonomies describing the modes of representation, it remains unclear what factors motivate certain depicting techniques over others. Results from a pantomime generation task show that pantomimes are not entirely idiosyncratic but rather follow generalisable patterns constrained by their semantic category. We show that a) specific modes of representations are preferred for certain objects (acting for manipulable objects and drawing for non-manipulable objects); and b) that use and ordering of deictics and modes of representation operate in tandem to distinguish between semantically related concepts (e.g., “to drink” vs “mug”). This study provides yet more evidence that our ability to communicate through silent gesture reveals systematic ways to describe events and objects around us

Abstract

Iconicity is a fundamental feature of human language. However its processing consequences at the behavioral and neural level in spoken word comprehension are not well understood. The current paper presents the behavioral and electrophysiological outcome of an auditory lexical decision task in which native speakers of Dutch listened to onomatopoeic words and matched control words while their electroencephalogram was recorded. Behaviorally, onomatopoeic words were processed as quickly and accurately as words with an arbitrary mapping between form and meaning. Event-related potentials time-locked to word onset revealed a significant decrease in negative amplitude in the N2 and N400 components and a late positivity for onomatopoeic words in comparison to the control words. These findings advance our understanding of the temporal dynamics of iconic form-meaning mapping in spoken word comprehension and suggest interplay between the neural representations of real-world sounds and spoken words.

Abstract

Two groups of participants were trained to perceive an ambiguous sound [s/f] as either /s/ or /f/ based on lexical bias: One group heard the ambiguous fricative in /s/-final words, the other in /f/-final words. This kind of exposure leads to a recalibration of the /s/-/f/ contrast [e.g., 4]. In order to investigate when and how this recalibration emerges, test trials were interspersed among training and filler trials. The learning effect needed at least 10 clear training items to arise. Its emergence seemed to occur in a rather step-wise fashion. Learning did not improve much after it first appeared. It is likely, however, that the early test trials attracted participants' attention and therefore may have interfered with the learning process.

Abstract

Word count estimation (WCE) from audio recordings has a number of applications, including quantifying the amount of speech that language-learning infants hear in their natural environments, as captured by daylong recordings made with devices worn by infants. To be applicable in a wide range of scenarios and also low-resource domains, WCE tools should be extremely robust against varying signal conditions and require minimal access to labeled training data in the target domain. For this purpose, earlier work has used automatic syllabification of speech, followed by a least-squares-mapping of syllables to word counts. This paper compares a number of previously proposed syllabifiers in the WCE task, including a supervised bi-directional long short-term memory (BLSTM) network that is trained on a language for which high quality syllable annotations are available (a “high resource language”), and reports how the alternative methods compare on different languages and signal conditions. We also explore additive noise and varying-channel data augmentation strategies for BLSTM training, and show how they improve performance in both matching and mismatching languages. Intriguingly, we also find that even though the BLSTM works on languages beyond its training data, the unsupervised algorithms can still outperform it in challenging signal conditions on novel languages.

Abstract

Infants, children and adults are capable of implicitly extracting regularities from their environment through statistical learning (SL). SL is present from early infancy and found across tasks and modalities, raising questions about the domain generality of SL. However, little is known about its’ developmental trajectory: Is SL fully developed capacity in infancy, or does it improve with age, like other cognitive skills? While SL is well established in infants and adults, only few studies have looked at SL across development with conflicting results: some find age-related improvements while others do not. Importantly, despite its postulated role in language learning, no study has examined the developmental trajectory of auditory SL throughout childhood. Here, we conduct a large-scale study of children's auditory SL across a wide age-range (5-12y, N=115). Results show that auditory SL does not change much across development. We discuss implications for modality-based differences in SL and for its role in language acquisition.

Abstract

Recent work suggests that cultural transmission can lead to the emergence of linguistic structure as speakers’ weak individual biases become amplified through iterated learning. However, to date, no published study has demonstrated a similar emergence of linguistic structure in children. This gap is problematic given that languages are mainly learned by children and that adults may bring existing linguistic biases to the task. Here, we conduct a large-scale study of iterated language learning in both children and adults, using a novel, child-friendly paradigm. The results show that while children make more mistakes overall, their languages become more learnable and show learnability biases similar to those of adults. Child languages did not show a significant increase in linguistic structure over time, but consistent mappings between meanings and signals did emerge on many occasions, as found with adults. This provides the first demonstration that cultural transmission affects the languages children and adults produce similarly.

Abstract

An exposure-test paradigm was used to examine whether Dutch listeners can adapt their perception to non-canonical marking of lexical stress in Hungarian-accented Dutch. During exposure, one group of listeners heard only words with correct initial stress, while another group also heard examples of unstressed initial syllables that were marked by high pitch, a possible stress cue in Dutch. Subsequently, listeners’ eye movements to target-competitor pairs with segmental overlap but different stress patterns were tracked while hearing Hungarian-accented Dutch. Listeners who had heard non-canonically produced words previously distinguished target-competitor pairs faster than listeners who had only been exposed to canonical forms before. This suggests that listeners can adapt quickly to speaker-specific realizations of non-canonical lexical stress.

Abstract

Listeners can flexibly retune category boundaries of their native language to adapt to non-canonically produced phonemes. This only occurs, however, if the pronunciation peculiarities can be attributed to stable and not transient speaker-specific characteristics. Listening to someone speaking a second language, listeners could attribute non-canonical pronunciations either to the speaker or to the fact that she is modifying her categories in the second language. We investigated whether, following exposure to Dutch-accented English, Dutch listeners show effects of category retuning during test where they hear the same speaker speaking her native language, Dutch. Exposure was a lexical-decision task where either word-final [f] or [s] was replaced by an ambiguous sound. At test listeners categorized minimal word pairs ending in sounds along an [f]-[s] continuum. Following exposure to English words, Dutch listeners showed boundary shifts of a similar magnitude as following exposure to the same phoneme variants in their native language. This suggests that production patterns in a second language are deemed a stable characteristic. A second experiment suggests that category retuning also occurs when listeners are exposed to and tested with a native speaker of their second language. Listeners thus retune phoneme boundaries across languages.

Abstract

The question of whether intonation events have a categorical mental representation has long been a puzzle in prosodic research, and one that experiments testing production and perception across category boundaries have failed to definitively resolve. This paper takes the alternative approach of looking for evidence of structure within a postulated category by testing for a Perceptual Magnet Effect (PME). PME has been found in boundary tones but has not previously been conclusively found in pitch accents. In this investigation, perceived goodness and discriminability of re-synthesised Dutch nuclear rise contours (L*H H%) were evaluated by naive native speakers of Dutch. The variation between these stimuli was quantified using a polynomial-parametric modelling approach (i.e. the SOCoPaSul model) in place of the traditional approach whereby excursion size, peak alignment and pitch register are used independently of each other to quantify variation between pitch accents. Using this approach to calculate the acoustic-perceptual distance between different stimuli, PME was detected: (1) rated goodness, decreased as acoustic-perceptual distance relative to the prototype increased, and (2) equally spaced items far from the prototype were less frequently generalised than equally spaced items in the neighbourhood of the prototype. These results support the concept of categorically distinct intonation events.

Abstract

Many previous studies have shown that both infants and adults are skilled statistical learners. Because statistical learning is affected by attention, learners' ability to manage their attention can play a large role in what they learn. However, it is still unclear how learners allocate their attention in order to gain information in a visual environment containing multiple objects, especially how prior visual experience (i.e., familiarly of objects) influences where people look. To answer these questions, we collected eye movement data from adults exploring multiple novel objects while manipulating object familiarity with global (frequencies) and local (repetitions) regularities. We found that participants are sensitive to both global and local statistics embedded in their visual environment and they dynamically shift their attention to prioritize some objects over others as they gain knowledge of the objects and their distributions within the task.

Abstract

For almost two decades, the poor performance observed with the so-called Director task has been interpreted as evidence of limited use of Theory of Mind in communication. Here we propose a probabilistic model of common ground in referential communication that derives three inferences from an utterance: what the speaker is talking about in a visual context, what she knows about the context, and what referential expressions she prefers. We tested our model by comparing its inferences with those made by human participants and found that it closely mirrors their judgments, whereas an alternative model compromising the hearer’s expectations of cooperativeness and efficiency reveals a worse fit to the human data. Rather than assuming that common ground is fixed in a given exchange and may or may not constrain reference resolution, we show how common ground can be inferred as part of the process of reference assignment.

Abstract

The current study reports the enhancing effect of a high variability training procedure in the learning of a Japanese geminate-singleton fricative contrast. Dutch natives took part in a five-day training procedure in which they identified geminate and singleton variants of the Japanese fricative /s/. They heard either many repetitions of a limited set of words recorded by a single speaker (simple training) or fewer repetitions of a more variable set of words recorded by multiple speakers (variable training). Pre-post identification evaluations and a transfer test indicated clear benefits of the variable training.

Abstract

While there are many studies on information retrieval models using full-text, there are presently no comparison studies of full-text retrieval vs. retrieval only over the titles of documents. On the one hand, the full-text of documents like scientific papers is not always available due to, e.g., copyright policies of academic publishers. On the other hand, conducting a search based on titles alone has strong limitations. Titles are short and therefore may not contain enough information to yield satisfactory search results. In this paper, we compare different retrieval models regarding their search performance on the full-text vs. only titles of documents. We use different datasets, including the three digital library datasets: EconBiz, IREON, and PubMed. The results show that it is possible to build effective title-based retrieval models that provide competitive results comparable to full-text retrieval. The difference between the average evaluation results of the best title-based retrieval models is only 3% less than those of the best full-text-based retrieval models.

Abstract

The use of word order and intonation to mark focus in child speech has received some attention. However, past work usually examined each device separately or only compared the realizations of focused vs. non-focused constituents. This paper investigates the interaction between word order and intonation in the marking of different focus types in 4- to 5-year old German-speaking children and an adult control group. An answer-reconstruction task was used to elicit syntactic (word order) and intonational focus marking of subject and objects (locus of focus) in three focus types (broad, narrow, and contrastive focus). The results indicate that both children and adults used intonation to distinguish broad from contrastive focus but they differed in the marking of narrow focus. Further, both groups preferred intonation to word order as device for focus marking. But children showed an early sensitivity for the impact of focus type and focus location on word order variation and on phonetic means to mark focus.

Abstract

Fine-Tracker is a speech-based model of human speech
recognition. While previous work has shown that Fine-Tracker
is successful at modelling aspects of human spoken-word
recognition, its speech recognition performance is not
comparable to that of human performance, possibly due to
suboptimal intermediate articulatory feature (AF)
representations. This study investigates the effect of improved
AF representations, obtained using a state-of-the-art deep
convolutional network, on Fine-Tracker’s simulation and
recognition performance: Although the improved AF quality
resulted in improved speech recognition; it, surprisingly, did
not lead to an improvement in Fine-Tracker’s simulation power.

Abstract

The current version of the psycholinguistic model of human word recognition Shortlist suffers from two unrealistic constraints. First, the input of Shortlist must consist of a single string of phoneme symbols. Second, the current version of the search in Shortlist makes it difficult to deal with insertions and deletions in the input phoneme string. This research attempts to fully automatically derive a phoneme string from the acoustic signal that is as close as possible to the number of phonemes in the lexical representation of the word. We optimised an Automatic Phone Recogniser (APR) using two approaches, viz. varying the value of the mismatch parameter and optimising the APR output strings on the output of Shortlist. The approaches show that it will be very difficult to satisfy the input requirements of the present version of Shortlist with a phoneme string generated by an APR.

Abstract

Previous research on lexically-guided perceptual learning has focussed on contrasts that differ primarily in local cues, such as plosive and fricative contrasts. The present research had two aims: to investigate whether perceptual learning occurs for a contrast with non-local cues, the /l/-/r/ contrast, and to establish whether STRAIGHT can be used to create ambiguous sounds on an /l/-/r/ continuum. Listening experiments showed lexically-guided learning about the /l/-/r/ contrast. Listeners can thus tune in to unusual speech sounds characterised by non-local cues. Moreover, STRAIGHT can be used to create stimuli for perceptual learning experiments, opening up new research possibilities. Index Terms: perceptual learning, morphing, liquids, human word recognition, STRAIGHT.

Abstract

Due to pronunciation variation, many insertions and deletions of phones occur in spontaneous speech. The psycholinguistic model of human speech recognition Shortlist is not well able to deal with phone insertions and deletions and is therefore not well suited for dealing with real-life input. The research presented in this paper explains how Shortlist can benefit from pronunciation variation modelling in dealing with real-life input. Pronunciation variation was modelled by including variants into the lexicon of Shortlist. A series of experiments was carried out to find the optimal acoustic model set for transcribing the training material that was used as basis for the generation of the variants. The Shortlist experiments clearly showed that Shortlist benefits from pronunciation variation modelling. However, the performance of Shortlist stays far behind the performance of other, more conventional speech recognisers.

Abstract

The time course of metrical encoding, i.e. stress, during speech production is investigated. In a first experiment, participants were presented with pictures whose bisyllabic Dutch names had initial or final stress (KAno 'canoe' vs. kaNON 'cannon'; capital letters indicate stressed syllables). Picture names were matched for frequency and object recognition latencies. When participants were asked to judge whether picture names had stress on the first or second syllable, they showed significantly faster decision times for initially stressed targets than for targets with final stress. Experiment 2 replicated this effect with trisyllabic picture names (faster RTs for penultimate stress than for ultimate stress). In our view, these results reflect the incremental phonological encoding process. Wheeldon and Levelt (1995) found that segmental encoding is a process running from the beginning to the end of words. Here, we present evidence that the metrical pattern of words, i.e. stress, is also encoded incrementally.

Abstract

The representative corpus SYN2000 in the Czech National Corpus (CNK) project containing 100 million word forms taken from different types of texts. I have tried to determine the extent and depth of the linguistic material in the corpus. First, I chose the adjectives indicating the basic colors of the spectrum and other parts of speech (names and adverbs) derived from these adjectives. An analysis of three examples - black, white and red - shows the extent of the linguistic wealth and diversity we are looking at: because of size limitations, no existing dictionary is capable of embracing all analyzed nuances. Currently, we can only hope that the next dictionary of contemporary Czech, built on the basis of the Czech National Corpus, will be electronic. Without the size limitations, we would be able us to include many of the fine nuances of language

Abstract

ELAN is a multimedia annotation tool that has been developed for roughly ten years now and is still being extended and improved in, on average, two or three major updates per year. This paper describes the current state of the application, the main areas of attention of the past few years and the plans for the near future. The emphasis will be on various interoperability issues: interoperability with other tools through file conversions, process based interoperability with other tools by means of commands send to or received from other applications, interoperability on the level of the data model and semantic interoperability.

Abstract

ELAN is a multimedia annotation tool developed by the Max Planck Institute for Psycholinguistics. It is applied in a variety of research areas. This paper presents a general overview of the tool and new developments as the calculation of inter-rater reliability, a commentary framework, semi-automatic segmentation and labeling and export to Theme.

Abstract

Computational models of reading have differed in terms of whether they propose a single route forming the mapping between orthography and phonology or whether there is a lexical/sublexical route distinction. A critical test of the architecture of the reading system is how it deals with multi-letter graphemes. Rastle and Coltheart (1998) found that the presence of digraphs in nonwords but not in words led to an increase in naming times, suggesting that nonwords were processed via a distinct sequential route to words. In contrast Pagliuca, Monaghan, and McIntosh (2008) implemented a single route model of reading and showed that under conditions of visual noise the presence of digraphs in words did have an effect on naming accuracy. In this study, we investigated whether such digraph effects could be found in both words and nonwords under conditions of visual noise. If so it would suggest that effects on words and nonwords are comparable. A single route connectionist model of reading showed greater accuracy for both words and nonwords containing digraphs. Experimental results showed participants were more accurate in recognising words if they contained digraphs. However contrary to model predictions they were less accurate in recognising nonwords containing digraphs compared to controls. We discuss the challenges faced by both theoretical perspectives in interpreting these findings and in light of a psycholinguistic grain size theory of reading.

Abstract

We conceptualize objects based on sensory and motor information gleaned from real-world experience. But to what extent is such conceptual information structured according to higher level linguistic features too? Here we investigate whether classifiers, a grammatical category, shape the conceptual representations of objects. In three experiments native Mandarin speakers (speakers of a classifier language) and native Dutch speakers (speakers of a language without classifiers) judged the similarity of a target object (presented as a word or picture) with four objects (presented as words or pictures). One object shared a classifier with the target, the other objects did not, serving as distractors. Across all experiments, participants judged the target object as more similar to the object with the shared classifier than distractor objects. This effect was seen in both Dutch and Mandarin speakers, and there was no difference between the two languages. Thus, even speakers of a non-classifier language are sensitive to object similarities underlying classifier systems, and using a classifier system does not exaggerate these similarities. This suggests that classifier systems simply reflect, rather than affect, conceptual structure.

Abstract

We report an individual with music-odor synaesthesia who experiences automatic and vivid odor sensations when she hears music. S’s odor associations were recorded on two days, and compared with those of two control participants. Overall, S produced longer descriptions, and her associations were of multiple odors at once, in comparison to controls who typically reported a single odor. Although odor associations were qualitatively different between S and controls, ratings of the consistency of their descriptions did not differ. This demonstrates that crossmodal associations between music and odor exist in non-synaesthetes too. We also found that S is better at discriminating between odors than control participants, and is more likely to experience emotion, memories and evaluations triggered by odors, demonstrating the broader impact of her synaesthesia.

Abstract

Language interacts with olfaction in exceptional ways. Olfaction is believed to be weakly linked with language, as demonstrated by our poor odor naming ability, yet olfaction seems to be particularly susceptible to linguistic descriptions. We tested the boundaries of the influence of language on olfaction by focusing on a non-lexical aspect of language (grammatical gender). We manipulated the grammatical gender of fragrance descriptions to test whether the congruence with fragrance gender would affect the way fragrances were perceived and remembered. Native French and German speakers read descriptions of fragrances containing ingredients with feminine or masculine grammatical gender, and then smelled masculine or feminine fragrances and rated them on a number of dimensions (e.g., pleasantness). Participants then completed an odor recognition test. Fragrances were remembered better when presented with descriptions whose grammatical gender matched the gender of the fragrance. Overall, results suggest grammatical manipulations of odor descriptions can affect odor cognition

Abstract

Mirror mimicry has well-known consequences for the person being mimicked: it increases how positively they feel about the mimicker (the Chameleon Effect). Here we show that anatomical mimicry has the opposite social consequences: a Reverse-Chameleon Effect. To equate mirror and anatomical mimicry, we asked participants to have a face-to-face conversation with a digital human (VIRTUO), in a fully-immersive virtual environment. Participants’ spontaneous head movements were tracked, and VIRTUO mimicked them at a 2-second delay, either mirror-wise, anatomically, or not at all (instead enacting another participant’s movements). Participants who were mimicked mirror-wise rated their social interaction with VIRTUO to be significantly more positive than those who were mimicked anatomically. Participants who were not mimicked gave intermediate ratings. Beyond its practical implications, the Reverse-Chameleon Effect constrains theoretical accounts of how mimicry affects social perception

Abstract

The Language Archive manages one of the largest and most varied sets of natural language data. This data consists of video and audio enriched with annotations. It is available for more than 250 languages, many of which are endangered. Researchers have a need to access this data conveniently and efficiently. We provide several browse and search methods to cover this need, which have been developed and expanded over the years. Metadata and content-oriented search methods can be connected for a more focused search. This article aims to provide a complete overview of the available search mechanisms, with a focus on annotation content search, including a benchmark.

Abstract

Do listeners use lexical stress at an early stage in word learning? Artificial-lexicon studies have shown that listeners can learn new spoken words easily. These studies used non-words differing in consonants and/or vowels, but not differing only in stress. If listeners use stress information in word learning, they should be able to learn new words that differ only in stress (e.g., BInulo-biNUlo). We investigated this issue here. When learning new words, Italian listeners relied on segmental information; they did not take stress information into account. Newly-acquired words differing in stress alone are not automatically represented as different words.

Abstract

Sequences of reaction times (RT) produced by participants in an experiment are not only influenced by the stimuli, but by many other factors as well, including fatigue, attention, experience, IQ, handedness, etc. These confounding factors result in longterm effects (such as a participant’s overall reaction capability) and in short- and medium-time fluctuations in RTs (often referred to as ‘local speed effects’). Because stimuli are usually presented in a random sequence different for each participant, local speed effects affect the underlying ‘true’ RTs of specific trials in different ways across participants. To be able to focus statistical analysis on the effects of the cognitive process under study, it is necessary to reduce the effect of confounding factors as much as possible. In this paper we propose and compare techniques and criteria for doing so, with focus on reducing (‘filtering’) the local speed effects. We show that filtering matters substantially for the significance analyses of predictors in linear mixed effect regression models. The performance of filtering is assessed by the average between-participant correlation between filtered RT sequences and by Akaike’s Information Criterion, an important measure of the goodness-of-fit of linear mixed effect regression models.

Abstract

This paper combines two different approaches to modeling reaction time data from lexical decision experiments, viz. a dataoriented statistical analysis by means of a linear mixed effects model, and a process-oriented computational model of human speech comprehension. The linear mixed effect model is implemented by lmer in R. As computational model we apply DIANA, an end-to-end computational model which aims at modeling the cognitive processes underlying speech comprehension. DIANA takes as input the speech signal, and provides as output the orthographic transcription of the stimulus, a word/non-word judgment and the associated reaction time. Previous studies have shown that DIANA shows good results for large-scale lexical decision experiments in Dutch and North-American English. We investigate whether predictors that appear significant in an lmer analysis and processes implemented in DIANA can be related and inform both approaches. Predictors such as ‘previous reaction time’ can be related to a process description; other predictors, such as ‘lexical neighborhood’ are hard-coded in lmer and emergent in DIANA. The analysis focuses on the interaction between subject variables and task variables in lmer, and the ways in which these interactions can be implemented in DIANA.

Abstract

This paper presents a method to quantify the spectral characteristics of reduction in speech. Hämäläinen et al. (2009) proposes a measure of spectral reduction which is able to predict a substantial amount of the variation in duration that linguistically motivated variables do not account for. In this paper, we continue studying acoustic reduction in speech by developing a new acoustic measure of reduction, based on local manifold structure in speech. We show that this measure yields significantly improved statistical models for predicting variation in duration.

Abstract

The recent advent of deep learning techniques in speech tech-nology and in particular in automatic speech recognition hasyielded substantial performance improvements. This suggeststhat deep neural networks (DNNs) are able to capture structurein speech data that older methods for acoustic modeling, suchas Gaussian Mixture Models and shallow neural networks failto uncover. In image recognition it is possible to link repre-sentations on the first couple of layers in DNNs to structuralproperties of images, and to representations on early layers inthe visual cortex. This raises the question whether it is possi-ble to accomplish a similar feat with representations on DNNlayers when processing speech input. In this paper we presentthree different experiments in which we attempt to untanglehow DNNs encode speech signals, and to relate these repre-sentations to phonetic knowledge, with the aim to advance con-ventional phonetic concepts and to choose the topology of aDNNs more efficiently. Two experiments investigate represen-tations formed by auto-encoders. A third experiment investi-gates representations on convolutional layers that treat speechspectrograms as if they were images. The results lay the basisfor future experiments with recursive networks.

Abstract

In spontaneous conversations, words are often produced in reduced form compared to formal careful speech. In English, for instance, ’probably’ may be pronounced as ’poly’ and ’police’ as ’plice’. Reduced forms are very common, and native listeners usually do not have any problems with interpreting these reduced forms in context. Non-native listeners, however, have great difficulties in comprehending reduced forms. In order to investigate the problems in comprehension that non-native listeners experience, a dictation experiment was conducted in which sentences were presented auditorily to non-natives either in full (unreduced) or reduced form. The types of errors made by the L2 listeners reveal aspects of the cognitive processes underlying this dictation task. In addition, we compare the errors made by these human participants with the type of word errors made by DIANA, a recently developed computational model of word comprehension.

Abstract

We estimate lexical Concreteness for millions of words across 77 languages. Using a simple regression framework, we combine vector-based models of lexical semantics with experimental norms of Concreteness in English and Dutch. By applying techniques to align vector-based semantics across distinct languages, we compute and release Concreteness estimates at scale in numerous languages for which experimental norms are not currently available. This paper lays out the technique and its efficacy. Although this is a difficult dataset to evaluate immediately, Concreteness estimates computed from English correlate with Dutch experimental norms at $\rho$ = .75 in the vocabulary at large, increasing to $\rho$ = .8 among Nouns. Our predictions also recapitulate attested relationships with word frequency. The approach we describe can be readily applied to numerous lexical measures beyond Concreteness