Publications

Abstract

Thesaurus alignment plays an important role in realising efficient access to heterogeneous Cultural Heritage data. Current ontology alignment techniques, however, provide only limited value for such access as they consider little if any requirements from realistic use cases or application scenarios. In this paper, we focus on two real-world scenarios in a library context: thesaurus merging and book re-indexing. We identify their particular requirements and describe our approach of deploying and evaluating thesaurus alignment techniques in this context. We have applied our approach for the Ontology Alignment Evaluation Initiative, and report on the performance evaluation of participants’ tools wrt. the application scenario at hand. It shows that evaluations of tools requires significant effort, but when done carefully, brings many benefits.

Abstract

A common assumption in the cognitive sciences is that artificial and natural language learning rely on shared mechanisms. However, attempts to bridge the two have yielded ambiguous results. We suggest that an empirical disconnect between the computations employed during learning and the methods employed at test may explain these mixed results. Further, we propose statistically-based chunking as a potential computational link between artificial and natural language learning. We compare the acquisition of non-adjacent dependencies to that of natural language structure using two types of tasks: reflection-based 2AFC measures, and processing-based recall measures, the latter being more computationally analogous to the processes used during language acquisition. Our results demonstrate that task-type significantly influences the correlations observed between artificial and natural language acquisition, with reflection-based and processing-based measures correlating within – but not across – task-type. These findings have fundamental implications for artificial-to-natural language comparisons, both methodologically and theoretically.

Abstract

Attempts to connect individual differences in statistical learning with broader aspects of cognition have received considerable attention, but have yielded mixed results. A possible explanation is that statistical learning is typically tested using the two-alternative forced choice (2AFC) task. As a meta-cognitive task relying on explicit familiarity judgments, 2AFC may not accurately capture implicitly formed statistical computations. In this paper, we adapt the classic serial-recall memory paradigm to implicitly test statistical learning in a statistically-induced chunking recall (SICR) task. We hypothesized that artificial language exposure would lead subjects to chunk recurring statistical patterns, facilitating recall of words from the input. Experiment 1 demonstrates that SICR offers more fine-grained insights into individual differences in statistical learning than 2AFC. Experiment 2 shows that SICR has higher test-retest reliability than that reported for 2AFC. Thus, SICR offers a more sensitive measure of individual differences, suggesting that basic chunking abilities may explain statistical learning.

Abstract

Adult-to-child interactions are often characterized by prosodically-exaggerated speech accompanied by visually captivating co-speech gestures. In a series of adult studies, we have shown that these gestures are linked in a sophisticated manner to the prosodic structure of adults' utterances. In the current study, we use the Preferential Looking Paradigm to demonstrate that two-year-olds can use the alignment of these gestures to speech to deduce the meaning of words.

Abstract

It has been suggested that some individuals may benefit more from social robots than do others. Using second
language (L2) as an example, the present study examined how individual differences in attitudes toward robots and personality
traits may be related to learning outcomes. Preliminary results with 24 Turkish-speaking adults suggest that negative attitudes
toward robots, more specifically thoughts and anxiety about the negative social impact that robots may have on the society,
predicted how well adults learned L2 words from a social robot. The possible implications of the findings as well as future directions are also discussed

Abstract

In production, frequently used words are preferentially extended to new, though related meanings. In comprehension, frequent exposure to a word instead makes the learner confident that all of the word’s legitimate uses have been experienced, resulting in an entrenched form-meaning mapping between the word and its experienced meaning(s). This results in a perception-production dissociation, where the forms speakers are most likely to map onto a novel meaning are precisely the forms that they believe can never be used that way. At first glance, this result challenges the idea of bidirectional form-meaning mappings, assumed by all current approaches to linguistic theory. In this paper, we show that bidirectional form-meaning mappings are not in fact challenged by this production-perception dissociation. We show that the production-perception dissociation is expected even if learners of the lexicon acquire simple symmetrical form-meaning associations through simple Hebbian learning.

Abstract

There is a strong relation between children’s exposure to
spatial terms and their later memory accuracy. In the current
study, we tested whether the production of spatial terms by
children themselves predicts memory accuracy and whether
and how language modality of these encodings modulates
memory accuracy differently. Hearing child speakers of
Turkish and deaf child signers of Turkish Sign Language
described pictures of objects in various spatial relations to each
other and later tested for their memory accuracy of these
pictures in a surprise memory task. We found that having
described the spatial relation between the objects predicted
better memory accuracy. However, the modality of these
descriptions in sign, speech, or speech-plus-gesture did not
reveal differences in memory accuracy. We discuss the
implications of these findings for the relation between spatial
language, memory, and the modality of encoding.

Abstract

Deaf children born to hearing parents are exposed to language input quite late, which has long-lasting effects on language production. Previous studies with deaf individuals mostly focused on linguistic expressions of motion events, which have several event components. We do not know if similar effects emerge in simple events such as descriptions of spatial configurations of objects. Moreover, previous data mainly come from late adult signers. There is not much known about language development of late signing children soon after learning sign language. We compared simple event descriptions of late signers of Turkish Sign Language (adults, children) to age-matched native signers. Our results indicate that while late signers in both age groups are native-like in frequency of expressing a relational encoding, they lag behind native signers in using morphologically complex linguistic forms compared to other simple forms. Late signing children perform similar to adults and thus showed no development over time.

Abstract

English and German have similar prosody, but their speakers realize some pitch falls (not rises) in subtly different ways. We here test for asymmetry in perception. An ABX discrimination task requiring F0 slope or duration judgements on isolated vowels revealed no cross-language difference in duration or F0 fall discrimination, but discrimination of rises (realized similarly in each language) was less accurate for English than for German listeners. This unexpected finding may reflect greater sensitivity to rising patterns by German listeners, or reduced sensitivity by English listeners as a result of extensive exposure to phrase-final rises (“uptalk”) in their language

Abstract

The ”download first, then process paradigm” is still the predominant working method amongst the research community. The web-based paradigm, however, offers many advantages from a tool development and data management perspective as they allow a quick adaptation to changing research environments. Moreover, new ways of combining tools and data are increasingly becoming available and will eventually enable a true web-based workflow approach, thus challenging the ”download first, then process” paradigm. The necessary infrastructure for managing, exploring and enriching language resources via the Web will need to be delivered by projects like CLARIN and DARIAH

Abstract

In this paper, we describe a unifying approach to tackle data heterogeneity issues for lexica and related resources. We present LEXUS, our software that implements the Lexical Markup Framework (LMF) to uniformly describe and manage lexica of different structures. LEXUS also makes use of a central Data Category Registry (DCR) to address terminological issues with regard to linguistic concepts as well as the handling of working and object languages. Finally, we report on ViCoS, a LEXUS extension, providing support for the definition of arbitrary semantic relations between lexical entries or parts thereof.

Abstract

To achieve true interoperability for valuable linguistic resources different levels of variation need to be addressed. ISO Technical Committee 37, Terminology and other language and content resources, is developing a Data Category Registry. This registry will provide a reusable set of data categories. A new implementation, dubbed ISOcat, of the registry is currently under construction. This paper shortly describes the new data model for data categories that will be introduced in this implementation. It goes on with a sketch of the standardization process. Completed data categories can be reused by the community. This is done by either making a selection of data categories using the ISOcat web interface, or by other tools which interact with the ISOcat system using one of its various Application Programming Interfaces. Linguistic resources that use data categories from the registry should include persistent references, e.g. in the metadata or schemata of the resource, which point back to their origin. These data category references can then be used to determine if two or more resources share common semantics, thus providing a level of interoperability close to the source data and a promising layer for semantic alignment on higher levels

Abstract

Two types of phonological variation in Dutch, resulting from optional rules, are schwa epenthesis and schwa deletion. In a lexical decision experiment it was investigated whether the phonological variants were processed similarly to the standard forms. It was found that the two types of variation patterned differently. Words with schwa epenthesis were processed faster and more accurately than the standard forms, whereas words with schwa deletion led to less fast and less accurate responses. The results are discussed in relation to the role of consonant-vowel alternations in speech processing and the perceptual integrity of onset clusters.

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

Using real-time eye-movement measures, we asked how a fantastical discourse context competes with stored representations of semantic and world knowledge to influence children's and adults' moment-by-moment interpretation of a story. Seven-year- olds were less effective at bypassing stored semantic and world knowledge during real-time interpretation than adults. Nevertheless, an effect of discourse context on comprehension was still apparent.

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

Digital processing of medical images has helped physicians and patients during past years by allowing examination and diagnosis on a very precise level. Nowadays possibly the biggest deal of support it can offer for modern healthcare is the use of high performance computing architectures to treat the huge amounts of data that can be collected by modern acquisition devices. This paper presents a parallel processing implementation of an image segmentation algorithm that operates on a computer cluster equipped with 10 processing units. Thanks to well-organized distribution of the workload we manage to significantly shorten the execution time of the developed algorithm and reach a performance gain very close to linear.

Abstract

Previous research has shown that repeated interactions can cause iconicity in signals to reduce. However, data from several recent studies has shown the opposite trend: an increase in iconicity as the result of repeated interactions. Here, we discuss whether signals may become less or more iconic as a result of the modality used to produce them. We review several recent experimental results before presenting new data from multi-modal signals, where visual input creates audio feedback. Our results show that the growth in iconicity present in the audio information may come at a cost to iconicity in the visual information. Our results have implications for how we think about and measure iconicity in artificial signalling experiments. Further, we discuss how iconicity in real world speech may stem from auditory, kinetic or visual information, but iconicity in these different modalities may conflict.

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

Young children demonstrate the ability to make inferences about the preferences of other agents based on their choices. However, there exists no overarching account of what children are doing when they learn about preferences or how they use that knowledge. We use a rational model of preference learning, drawing on ideas from economics and computer science, to explain the behavior of children in several recent experiments. Specifically, we show how a simple econometric model can be extended to capture two- to four-year-olds’ use of statistical information in inferring preferences, and their generalization of these preferences.

Abstract

We examined how communicators can switch between speaker and listener role with such accurate timing. During conversations, the majority of role transitions happens with a gap or overlap of only a few hundred milliseconds. This suggests that listeners can predict when the turn of the current speaker is going to end. Our hypothesis is that listeners know when a turn ends because they know how it ends. Anticipating the last words of a turn can help the next speaker in predicting when the turn will end, and also in anticipating the content of the turn, so that an appropriate response can be prepared in advance. We used the stimuli material of an earlier experiment (De Ruiter, Mitterer & Enfield, 2006), in which subjects were listening to turns from natural conversations and had to press a button exactly when the turn they were listening to ended. In the present experiment, we investigated if the subjects can complete those turns when only an initial fragment of the turn is presented to them. We found that the subjects made better predictions about the last words of those turns that had more accurate responses in the earlier button press experiment.

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

Visual and auditory channels have different affordances and
this is mirrored in what information is available for linguistic
encoding. The visual channel has high spatial acuity, whereas
the auditory channel has better temporal acuity. These
differences may lead to different conceptualizations of events
and affect multimodal language production. Previous studies of
motion events typically present visual input to elicit speech and
gesture. The present study compared events presented as audio-
only, visual-only, or multimodal (visual+audio) input and
assessed speech and co-speech gesture for path and manner of
motion in Turkish. Speakers with audio-only input mentioned
path more and manner less in verbal descriptions, compared to
speakers who had visual input. There was no difference in the
type or frequency of gestures across conditions, and gestures
were dominated by path-only gestures. This suggests that input
modality influences speakers’ encoding of path and manner of
motion events in speech, but not in co-speech gestures.

Abstract

Speech rate is known to modulate perception of temporally ambiguous speech sounds. For instance, a vowel may be perceived as short when the immediate speech context is slow, but as long when the context is fast. Yet, effects of long-term tracking of speech rate are largely unexplored. Two experiments tested whether long-term tracking of rate influences perception of the temporal Dutch vowel contrast /ɑ/-/a:/. In Experiment 1, one low-rate group listened to 'neutral' rate speech from talker A and to slow speech from talker B. Another high-rate group was exposed to the same neutral speech from A, but to fast speech from B. Between-group comparison of the 'neutral' trials revealed that the low-rate group reported a higher proportion of /a:/ in A's 'neutral' speech, indicating that A sounded faster when B was slow. Experiment 2 tested whether one's own speech rate also contributes to effects of long-term tracking of rate. Here, talker B's speech was replaced by playback of participants' own fast or slow speech. No evidence was found that one's own voice affected perception of talker A in larger speech contexts. These results carry implications for our understanding of the mechanisms involved in rate-dependent speech perception and of dialogue.

Abstract

Recurrent neural networks (RNNs) have long been an architecture of interest for computational models of human sentence processing. The recently introduced Transformer architecture outperforms RNNs on many natural language processing tasks but little is known about its ability to model human language processing. We compare Transformer- and RNN-based language models’ ability to account for measures of human reading effort. Our analysis shows Transformers to outperform RNNs in explaining self-paced reading times and neural activity during reading English sentences, challenging the widely held idea that human sentence processing involves recurrent and immediate processing and provides evidence for cue-based retrieval.

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

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

Words are reduced in spontaneous speech. If reductions are constrained by functional (i.e., perception and production) constraints, they should not be arbitrary. This hypothesis was tested by examing the pronunciations of high- to mid-frequency words in a Dutch and a German spontaneous speech corpus. In logistic-regression models the "reduction likelihood" of a phoneme was predicted by fixed-effect predictors such as position within the word, word length, word frequency, and stress, as well as random effects such as phoneme identity and word. The models for Dutch and German show many communalities. This is in line with the assumption that similar functional constraints influence reductions in both languages.

Abstract

Learning how words refer to aspects of the environment is a complex task, but one that is supported by numerous cues within the environment which constrain the possibilities for matching words to their intended referents. In this paper we tested the predictions of a computational model of multiple cue integration for word learning, that predicted variation in the presence of cues provides an optimal learning situation. In a cross-situational learning task with adult participants, we varied the reliability of presence of distributional, prosodic, and gestural cues. We found that the best learning occurred when cues were often present, but not always. The effect of variability increased the salience of individual cues for the learner, but resulted in robust learning that was not vulnerable to individual cues’ presence or absence. Thus, variability of multiple cues in the language-learning environment provided the optimal circumstances for word learning.

Abstract

This thesis is about whether language affects thinking. It deals with the linguistic relativity hypothesis, which proposes that the language we speak influences the way we think. This hypothesis is investigated in the domain of caused motion (e.g., ‘The man rolled the tyre into the garage’), by looking at Spanish and Swedish, two languages that show striking differences in how motion events are encoded. The thesis consists of four studies. The first two focus on native speakers of Spanish and Swedish. Study I compares how Spanish and Swedish speakers describe the same set of caused motion events, directing the spotlight at how variable the descriptions are in each language. The results confirm earlier findings from semantic typology regarding the dominant ways of expressing the events in each language: Spanish behaves like a verb-framed language and Swedish like a satellite-framed language (Talmy, 2000). Going beyond previous findings, the study demonstrates—using the tools of entropy and Monte Carlo simulations—that there is markedly more variability in Spanish than in Swedish descriptions. Study II tests whether differences in how Spanish and Swedish speakers describe caused motion events are reflected in how they think about such events. Using a novel similarity arrangement task, it is found that Spanish and Swedish speakers partly differ in how they represent caused motion events if they can access language during the task. However, the differences disappear when the possibility to use language is momentarily blocked by an interference task. The last two studies focus on Swedish learners of Spanish as a second language (L2). Study III explores how Swedish learners (compared to native Spanish speakers) adapt their Spanish motion descriptions to recently encountered input. Using insights from the literature on structural priming, we find that Swedish learners initially expect to encounter in their L2, Spanish, those verb types that are typical in Swedish (manner verbs like ‘roll’) but that, with increasing proficiency, their expectations become increasingly attuned to the typical Spanish pattern of using path verbs (like ‘enter’). These expectations are reflected in the way L2 learners adapt their own production to the Spanish input. Study IV asks whether recent linguistic experience in an L2 can affect how L2 learners think about motion events. It is found that encountering motion descriptions in the L2 that emphasize different types of information (path or manner) leads L2 speakers to perceive similarity along different dimensions in a subsequent similarity arrangement task. Taken together, the thesis argues that the study of the relation between language and thought affords more valuable insights when not posed as an either-or question (i.e., does language affect thought or not?). In this spirit, the thesis contributes to the wider aim of investigating the conditions under which language does or does not affect thought and explores what the different outcomes tell us about language, thought, and the intricate mechanisms that relate them.

Abstract

Language emergence is characterized by a high degree of lex-
ical variation. It has been suggested that the speed at which
lexical conventionalization occurs depends partially on social
structure. In large communities, individuals receive input from
many sources, creating a pressure for lexical convergence.
In small, insular communities, individuals can remember id-
iolects and share common ground with interlocuters, allow-
ing these communities to retain a high degree of lexical vari-
ation. We look at lexical variation in Kata Kolok, a sign lan-
guage which emerged six generations ago in a Balinese vil-
lage, where women tend to have more tightly-knit social net-
works than men. We test if there are differing degrees of lexical
uniformity between women and men by reanalyzing a picture
description task in Kata Kolok. We find that women’s produc-
tions exhibit less lexical uniformity than men’s. One possible
explanation of this finding is that women’s more tightly-knit
social networks allow for remembering idiolects, alleviating
the pressure for lexical convergence, but social network data
from the Kata Kolok community is needed to support this ex-
planation.

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

Sign languages stand out in that there is high prevalence of
conventionalised linguistic forms that map directly to their
referent (i.e., iconic). Hearing adults show low performance
when asked to guess the meaning of iconic signs suggesting
that their iconic features are largely inaccessible to them.
However, it has not been investigated whether speakers’
gestures, which also share the property of iconicity, may
assist non-signers in guessing the meaning of signs. Results
from a pantomime generation task (Study 1) show that
speakers’ gestures exhibit a high degree of systematicity, and
share different degrees of form overlap with signs (full,
partial, and no overlap). Study 2 shows that signs with full
and partial overlap are more accurately guessed and are
assigned higher iconicity ratings than signs with no overlap.
Deaf and hearing adults converge in their iconic depictions
for some concepts due to the shared conceptual knowledge
and manual-visual modality.

Abstract

This paper examines the use of iconic words in early conversations between children and caregivers. The longitudinal data include a span of six observations of 35 children-parent dyads in the same semi-structured activity. Our findings show that children’s speech initially has a high proportion of iconic words, and over time, these words become diluted by an increase of arbitrary words. Parents’ speech is also initially high in iconic words, with a decrease in the proportion of iconic words over time – in this case driven by the use of fewer iconic words. The level and development of iconicity are related to individual differences in the children’s cognitive skills. Our findings fit with the hypothesis that iconicity facilitates early word learning and may play an important role in learning to produce new words.

Abstract

Cognitive neuroscience approaches the brain as a cognitive system: a system that functionally is conceptualized in terms of information processing. We outline some aspects of this concept and consider a physical system to be an information processing device when a subclass of its physical states can be viewed as representational/cognitive and transitions between these can be conceptualized as a process operating on these states by implementing operations on the corresponding representational structures. We identify a generic and fundamental problem in cognition: sequentially organized structured processing. Structured sequence processing provides the brain, in an essential sense, with its processing logic. In an approach addressing this problem, we illustrate how to integrate levels of analysis within a framework of adaptive dynamical systems. We note that the dynamical system framework lends itself to a description of asynchronous event-driven devices, which is likely to be important in cognition because the brain appears to be an asynchronous processing system. We use the human language faculty and natural language processing as a concrete example through out.

Abstract

A key challenge for cognitive neuroscience is to decipher the representational schemes of the brain. A recent class of decoding algorithms for fMRI data, stimulus-feature-based encoding models, is becoming increasingly popular for inferring the dimensions of neural representational spaces from stimulus-feature spaces. We argue that such inferences are not always valid, because decoding can occur even if the neural representational space and the stimulus-feature space use different representational schemes. This can happen when there is a systematic mapping between them. In a simulation, we successfully decoded the binary representation of numbers from their decimal features. Since binary and decimal number systems use different representations, we cannot conclude that the binary representation encodes decimal features. The same argument applies to the decoding of neural patterns from stimulus-feature spaces and we urge caution in inferring the nature of the neural code from such methods. We discuss ways to overcome these inferential limitations.

Abstract

A classic discussion about visual imagery is whether it affords reinterpretation, like discovering two interpretations in the duck/rabbit illustration. Recent findings converge on reinterpretation being possible in visual imagery, suggesting functional equivalence with pictorial representations. However, it is unclear whether such reinterpretations are necessarily a visual-pictorial achievement. To assess this, 68 participants were briefly presented 2-d ambiguous figures. One figure was presented visually, the other via manual touch alone. Afterwards participants mentally rotated the memorized figures as to discover a novel interpretation. A portion (20.6%) of the participants detected a novel interpretation in visual imagery, replicating previous research. Strikingly, 23.6% of participants were able to reinterpret figures they had only felt. That reinterpretation truly involved haptic processes was further supported, as some participants performed co-thought gestures on an imagined figure during retrieval. These results are promising for further development of an Enactivist approach to imagination.

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

Dutch listeners' looks to printed words were tracked while they listened to instructions to click with their mouse on one of them. When presented with targets from word pairs where the first two syllables were segmentally identical but differed in stress location, listeners used stress information to recognize the target before segmental information disambiguated the words. Furthermore, the amount of lexical competition was influenced by the presence or absence of word-initial stress.

Abstract

Segmental as well as suprasegmental information is used by Dutch listeners to recognize words. The time-course of the effect of suprasegmental stress information on spoken-word recognition was investigated in a previous study, in which we tracked Dutch listeners' looks to arrays of four printed words as they listened to spoken sentences. Each target was displayed along with a competitor that did not differ segmentally in its first two syllables but differed in stress placement (e.g., 'CENtimeter' and 'sentiMENT'). The listeners' eye-movements showed that stress information is used to recognize the target before distinct segmental information is available. Here, we examine the role of durational information in this effect. Two experiments showed that initial-syllable duration, as a cue to lexical stress, is not interpreted dependent on the speaking rate of the preceding carrier sentence. This still held when other stress cues like pitch and amplitude were removed. Rather, the speaking rate of the preceding carrier affected the speed of word recognition globally, even though the rate of the target itself was not altered. Stress information modulated lexical competition, but did so independently of the rate of the preceding carrier, even if duration was the only stress cue present.

Abstract

The recognition of emotions of disgust, anger and fear have been shown to be significantly impaired in Huntington’s disease (eg,Sprengelmeyer et al, 1997, 2006; Gray et al, 1997; Milders et al, 2003,Montagne et al, 2006; Johnson et al, 2007; De Gelder et al, 2008). The relative impairment of these emotions might have implied a recognition impairment specific to negative emotions. Could the asymmetric recognition deficits be due not to the complexity of the emotion but rather reflect the complexity of the task? In the current study, 15 Huntington’s patients and 16 control subjects were presented with negative and positive non-speech emotional vocalisations that were to be identified as anger, fear, sadness, disgust, achievement, pleasure and amusement in a forced-choice paradigm. This experiment more accurately matched the negative emotions with positive emotions in a homogeneous modality. The resulting dually impaired ability of Huntington’s patients to identify negative and positive non-speech emotional vocalisations correctly provides evidence for an overall emotional recognition deficit in the disease. These results indicate that previous findings of a specificity in emotional recognition deficits might instead be due to the limitations of the visual modality. Previous experiments may have found an effect of emotional specificy due to the presence of a single positive emotion, happiness, in the midst of multiple negative emotions. In contrast with the previous literature, the study presented here points to a global deficit in the recognition of emotional sounds.

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

This paper presents the first application of polynomial modeling as a means for validating phonological pitch accent labels to German data. It is compared to traditional phonetic analysis (measuring minima, maxima, alignment). The traditional method fares better in classification, but results are comparable in statistical accent pair testing. Robustness tests show that pitch correction is necessary in both cases. The approaches are discussed in terms of their practicability, applicability to other domains of research and interpretability of their results.

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

In the context of the source-filter theory of speech, it is well established that intelligibility is heavily reliant on information carried by the filter, that is, spectral cues (e.g., Faulkner et al., 2001; Shannon et al., 1995). However, the extraction of other types of information in the speech signal, such as emotion and identity, is less well understood. In this study we investigated the extent to which emotion recognition in speech depends on filterdependent cues, using a forced-choice emotion identification task at ten levels of noise-vocoding ranging between one and 32 channels. In addition, participants performed a speech intelligibility task with the same stimuli. Our results indicate that compared to speech intelligibility, emotion recognition relies less on spectral information and more on cues typically signaled by source variations, such as voice pitch, voice quality, and intensity. We suggest that, while the reliance on spectral dynamics is likely a unique aspect of human speech, greater phylogenetic continuity across species may be found in the communication of affect in vocalizations.

Abstract

Research using event-related brain potentials (ERPs) has demonstrated an early differential effect in fronto-central regions when processing emotional, as compared to affectively neutral facial stimuli (e.g., Eimer & Holmes, 2002). In this talk, data demonstrating a similar effect in the auditory domain will be presented. ERPs were recorded in a one-back task where participants had to identify immediate repetitions of emotion category, such as a fearful sound followed by another fearful sound. The stimulus set consisted of non-verbal emotional vocalisations communicating positive and negative sounds, as well as neutral baseline conditions. Similarly to the facial domain, fear sounds as compared to acoustically controlled neutral sounds, elicited a frontally distributed positivity with an onset latency of about 150 ms after stimulus onset. These data suggest the existence of a rapid multi-modal frontocentral mechanism discriminating emotional from non-emotional human signals.

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

Listeners outperform ASR systems in every speech recognition task. However, what is not clear is where this human advantage originates. This paper investigates the role of acoustic feature representations. We test four (MFCCs, PLPs, Mel Filterbanks, Rate Maps) acoustic representations, with and without ‘pitch’ information, using the same backend. The results are compared with listener results at the level of articulatory feature classification. While no acoustic feature representation reached the levels of human performance, both MFCCs and Rate maps achieved good scores, with Rate maps nearing human performance on the classification of voicing. Comparing the results on the most difficult articulatory features to classify showed similarities between the humans and the SVMs: e.g., ‘dental’ was by far the least well identified by both groups. Overall, adding pitch information seemed to hamper classification performance.

Abstract

There is now considerable evidence that fine-grained acoustic-phonetic detail in the speech signal helps listeners to segment a speech signal into syllables and words. In this paper, we compare two computational models of word recognition on their ability to capture and use this finephonetic detail during speech recognition. One model, SpeM, is phoneme-based, whereas the other, newly developed Fine- Tracker, is based on articulatory features. Simulations dealt with modelling the ability of listeners to distinguish short words (e.g., ‘ham’) from the longer words in which they are embedded (e.g., ‘hamster’). The simulations with Fine- Tracker showed that it was, like human listeners, able to distinguish between short words from the longer words in which they are embedded. This suggests that it is possible to extract this fine-phonetic detail from the speech signal and use it during word recognition.

Abstract

This paper presents the results of a joint effort of a group of multimodality researchers and tool developers to improve the interoperability between several tools used for the annotation of multimodality. We propose a multimodal annotation exchange format, based on the annotation graph formalism, which is supported by import and export routines in the respective tools

Abstract

The INTERSPEECH 2017 Computational Paralinguistics Challenge addresses three different problems for the first time in research competition under well-defined conditions: In the Addressee sub-challenge, it has to be determined whether speech produced by an adult is directed towards another adult or towards a child; in the Cold sub-challenge, speech under cold has to be told apart from ‘healthy’ speech; and in the Snoring subchallenge, four different types of snoring have to be classified. In this paper, we describe these sub-challenges, their conditions, and the baseline feature extraction and classifiers, which include data-learnt feature representations by end-to-end learning with convolutional and recurrent neural networks, and bag-of-audiowords for the first time in the challenge series