Publications

Abstract

Words can be distinguished by segmental differences or by suprasegmental differences or both. Studies from English suggest that suprasegmentals play little role in human spoken-word recognition; English stress, however, is nearly always unambiguously coded in segmental structure (vowel quality); this relationship is less close in Dutch. The present study directly compared the effects of segmental and suprasegmental mispronunciation on word recognition in Dutch. There was a strong effect of suprasegmental mispronunciation, suggesting that Dutch listeners do exploit suprasegmental information in word recognition. Previous findings indicating the effects of mis-stressing for Dutch differ with stress position were replicated only when segmental change was involved, suggesting that this is an effect of segmental rather than suprasegmental processing.

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

Research in body language with use of gesture recognition and speech analysis has gained much attention in the recent times, influencing disciplines related to image and speech processing.

This study aims to design the Media Query Language (MQL) (Lenkiewicz, et al. 2012) combined with the Linguistic Media Query Interface (LMQI) for Elan (Wittenburg, et al. 2006). The system integrated with the new achievements in audio-video recognition will allow querying media files with predefined gesture phases (or motion primitives) and speech characteristics as well as combinations of both. For the purpose of this work the predefined motions and speech characteristics are called patterns for atomic elements and actions for a sequence of patterns. The main assumption is that a user-customized library of patterns and actions and automated media annotation with LMQI will reduce annotation time, hence decreasing costs of creation of annotated corpora. Increase of the number of annotated data should influence the speed and number of possible research in disciplines in which human multimodal interaction is a subject of interest and where annotated corpora are required.

Abstract

In different fields of the humanities annotations of multimodal resources are a necessary component of the research workflow. Examples include linguistics, psychology, anthropology, etc. However, creation of those annotations is a very laborious task, which can take 50 to 100 times the length of the annotated media, or more. This can be significantly improved by applying innovative audio and video processing algorithms, which analyze the recordings and provide automated annotations. This is the aim of the AVATecH project, which is a collaboration of the Max Planck Institute for Psycholinguistics (MPI) and the Fraunhofer institutes HHI and IAIS. In this paper we present a set of results of automated annotation together with an evaluation of their quality.

Abstract

Introduction Human spoken communication is multimodal, i.e. it encompasses both speech and gesture. Acoustic properties of voice, body movements, facial expression, etc. are an inherent and meaningful part of spoken interaction; they can provide attitudinal, grammatical and semantic information. In the recent years interest in audio-visual corpora has been rising rapidly as they enable investigation of different communicative modalities and provide more holistic view on communication (Kipp et al. 2009). Moreover, for some languages such corpora are the only available resource, as is the case for endangered languages for which no written resources exist.

Abstract

In this paper we describe audio and video processing algorithms that are developed in the scope of AVATecH project. The purpose of these algorithms is to shorten the time taken by manual annotation of audio and video recordings by extracting features from media files and creating semi-automated annotations. We show that the use of such supporting algorithms can shorten the annotation time to 30-50% of the time necessary to perform a fully manual annotation of the same kind.

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

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

Since the cognitive revolution, a widely held assumption has been that—whereas content may vary across cultures—cognitive processes would be universal, especially those on the more basic levels. Even if scholars do not fully subscribe to this assumption, they often conceptualize, or tend to investigate, cognition as if it were universal (Henrich, Heine, & Norenzayan, 2010). The insight that universality must not be presupposed but scrutinized is now gaining ground, and cognitive diversity has become one of the hot (and controversial) topics in the field (Norenzayan & Heine, 2005). We argue that, for scrutinizing the cultural dimension of cognition, taking an anthropological perspective is invaluable, not only for the task itself, but for attenuating the home-field disadvantages that are inescapably linked to cross-cultural research (Medin, Bennis, & Chandler, 2010).

Abstract

Scholars disagree about the extent to which language can tell us
about conceptualisation of the world. Some believe that language
is a direct window onto concepts: Having a word ‘‘bird’’, ‘‘table’’ or
‘‘sour’’ presupposes the corresponding underlying concept, BIRD,
TABLE, SOUR. Others disagree. Words are thought to be uninformative,
or worse, misleading about our underlying conceptual representations;
after all, our mental worlds are full of ideas that we
struggle to express in language. How could this be so, argue sceptics,
if language were a direct window on our inner life? In this presentation,
I consider what language can tell us about the
conceptualisation of taste. By considering linguistic data from
twenty unrelated cultures – varying in subsistence mode (huntergatherer
to industrial), ecological zone (rainforest jungle to desert),
dwelling type (rural and urban), and so forth – I argue any single language is, indeed, impoverished about what it can reveal about
taste. But recurrent lexicalisation patterns across languages can
provide valuable insights about human taste experience. Moreover,
language patterning is part of the data that a good theory of taste
perception has to be answerable for. Taste researchers, therefore,
cannot ignore the crosslinguistic facts.

Abstract

This Research Topic explores the question: what is the relationship between representations of time and space in cultures around the world? This question touches on the broader issue of how humans come to represent and reason about abstract entities – things we cannot see or touch. Time is a particularly opportune domain to investigate this topic. Across cultures, people use spatial representations for time, for example in graphs, time-lines, clocks, sundials, hourglasses, and calendars. In language, time is also heavily related to space, with spatial terms often used to describe the order and duration of events. In English, for example, we might move a meeting forward, push a deadline back, attend a long concert or go on a short break. People also make consistent spatial gestures when talking about time, and appear to spontaneously invoke spatial representations when processing temporal language. A large body of evidence suggests a close correspondence between temporal and spatial language and thought. However, the ways that people spatialize time can differ dramatically across languages and cultures. This research topic identifies and explores some of the sources of this variation, including patterns in spatial thinking, patterns in metaphor, gesture and other cultural systems. This Research Topic explores how speakers of different languages talk about time and space and how they think about these domains, outside of language. The Research Topic invites papers exploring the following issues: 1. Do the linguistic representations of space and time share the same lexical and morphosyntactic resources? 2. To what extent does the conceptualization of time follow the conceptualization of space?

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

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

Our knowledge of speech perception is largely based on experiments conducted with carefully recorded clear speech presented under good listening conditions to undistracted listeners - a near-ideal situation, in other words. But the reality poses a set of different challenges. First of all, listeners may need to divide their attention between speech comprehension and another task (e.g., driving). Outside the laboratory, the speech signal is often slurred by less than careful pronunciation and the listener has to deal with background noise. Moreover, in a globalized world, listeners need to understand speech in more than their native language. Relatedly, the speakers we listen to often have a different language background so we have to deal with a foreign or regional accent we are not familiar with. Finally, outside the laboratory, speech perception is not an end in itself, but rather a mean to contribute to a conversation. Listeners do not only need to understand the speech they are hearing, they also need to use this information to plan and time their own responses. For this special topic, we invite papers that address any of these ecological aspects of speech perception.

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

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

Studies have shown that listeners segmenting unfamiliar languages transfer native-language (L1) segmentation cues. These studies, however, conflated L1 and recent linguistic exposure. The present study investigates the relative influences of L1 and recent linguistic exposure on the use of prosodic cues for segmenting an artificial language (AL). Participants were L1-French listeners, high-proficiency L2-French L1-English listeners, and L1-English listeners without functional knowledge of French. The prosodic cue assessed was F0 rise, which is word-final in French, but in English tends to be word-initial. 30 participants heard a 20-minute AL speech stream with word-final boundaries marked by F0 rise, and decided in a subsequent listening task which of two words (without word-final F0 rise) had been heard in the speech stream. The analyses revealed a marginally significant effect of L1 (all listeners) and, importantly, a significant effect of recent linguistic exposure (L1-French and L2-French listeners): accuracy increased with decreasing time in the US since the listeners’ last significant (3+ months) stay in a French-speaking environment. Interestingly, no effect of L2 proficiency was found (L2-French listeners).

Abstract

Language resources can be divided into structural resources treating phonology, morphosyntax, semantics etc. and resources treating the social, demographic, ethnic, political context. A third type are meta-resources, like bibliographies, which provide access to the resources of the first two kinds. This poster will present the Glottolog/Langdoc project, a comprehensive bibliography providing web access to 180k bibliographical records to (mainly) low visibility resources from low-density languages. The resources are annotated for macro-area, content language, and document type and are available in XHTML and RDF.

Abstract

Natural sign languages and gestures are complex communicative systems that allow the incorporation of features of a referent into their structure. They differ, however, in that signs are more conventionalised because they consist of meaningless phonological parameters. There is some evidence that despite non-signers finding iconic signs more memorable they can have more difficulty at articulating their exact phonological components. In the present study, hearing non-signers took part in a sign repetition task in which they had to imitate as accurately as possible a set of iconic and arbitrary signs. Their renditions showed that iconic signs were articulated significantly less accurately than arbitrary signs. Participants were recalled six months later to take part in a sign generation task. In this task, participants were shown the English translation of the iconic signs they imitated six months prior. For each word, participants were asked to generate a sign (i.e., an iconic gesture). The handshapes produced in the sign repetition and sign generation tasks were compared to detect instances in which both renditions presented the same configuration. There was a significant correlation between articulation accuracy in the sign repetition task and handshape overlap. These results suggest some form of gestural interference in the production of iconic signs by hearing non-signers. We also suggest that in some instances non-signers may deploy their own conventionalised gesture when producing some iconic signs. These findings are interpreted as evidence that non-signers process iconic signs as gestures and that in production, only when sign and gesture have overlapping features will they be capable of producing the phonological components of signs accurately.

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

Dutch and Spanish differ in how predictable the stress pattern is as a function of the segmental content: it is correlated with syllable weight in Dutch but not in Spanish. In the present study, two experiments were run to compare the abilities of Dutch and Spanish speakers to separately process segmental and stress information. It was predicted that the Spanish speakers would have more difficulty focusing on the segments and ignoring the stress pattern than the Dutch speakers. The task was a speeded classification task on CVCV syllables, with blocks of trials in which the stress pattern could vary versus blocks in which it was fixed. First, we found interference due to stress variability in both languages, suggesting that the processing of segmental information cannot be performed independently of stress. Second, the effect was larger for Spanish than for Dutch, suggesting that that the degree of interference from stress variation may be partially mitigated by the predictability of stress placement in the language.

Abstract

In everyday communication, people not only use speech but
also hand gestures to convey information. One intriguing
question in gesture research has been why gestures take the
specific form they do. Previous research has identified the
speaker-gesturer’s communicative intent as one factor
shaping the form of iconic gestures. Here we investigate
whether communicative intent also shapes the form of
pointing gestures. In an experimental setting, twenty-four
participants produced pointing gestures identifying a referent
for an addressee. The communicative intent of the speakergesturer
was manipulated by varying the informativeness of
the pointing gesture. A second independent variable was the
presence or absence of concurrent speech. As a function of their communicative intent and irrespective of the presence of speech, participants varied the durations of the stroke and the post-stroke hold-phase of their gesture. These findings add to our understanding of how the communicative context influences the form that a gesture takes.

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

A fundamental ability of speakers is to
quickly retrieve words from long-term memory. According to a prominent theory, concepts activate multiple associated words, which enter into competition for selection. Previous electrophysiological studies have provided evidence for the activation of multiple alternative words, but did not identify brain responses refl ecting competition. We report a magnetoencephalography study examining the timing and neural substrates of lexical activation and competition. The degree of activation of competing words was
manipulated by presenting pictures (e.g., dog) simultaneously with distractor
words. The distractors were semantically related to the picture name (cat), unrelated (pin), or identical (dog). Semantic distractors are stronger competitors to the picture name, because they receive additional activation from the picture, whereas unrelated distractors do not. Picture naming times were longer with semantic than with unrelated and identical distractors. The patterns of phase-locked and non-phase-locked activity were distinct
but temporally overlapping. Phase-locked activity in left middle temporal
gyrus, peaking at 400 ms, was larger on unrelated than semantic and identical trials, suggesting differential effort in processing the alternative words activated by the picture-word stimuli. Non-phase-locked activity in the 4-10 Hz range between 400-650 ms in left superior frontal gyrus was larger on semantic than unrelated and identical trials, suggesting different
degrees of effort in resolving the competition among the alternatives
words, as refl ected in the naming times. These findings characterize distinct
patterns of brain activity associated with lexical activation and competition
respectively, and their temporal relation, supporting the theory that words are selected by competition.

Abstract

Two eye-tracking experiments tested whether native listeners can adapt
to reductions in casual Dutch speech. Listeners were exposed to segmental
([b] > [m]), syllabic (full-vowel-deletion), or no reductions. In a subsequent
test phase, all three listener groups were tested on how efficiently they could
recognize both types of reduced words. In the first Experiment’s exposure
phase, the (un)reduced target words were predictable. The segmental reductions
were completely consistent (i.e., involved the same input sequences).
Learning about them was found to be pattern-specific and generalized in the
test phase to new reduced /b/-words. The syllabic reductions were not consistent
(i.e., involved variable input sequences). Learning about them was
weak and not pattern-specific. Experiment 2 examined effects of word repetition
and predictability. The (un-)reduced test words appeared in the exposure
phase and were not predictable. There was no evidence of learning for
the segmental reductions, probably because they were not predictable during
exposure. But there was word-specific learning for the vowel-deleted words.
The results suggest that learning about reductions is pattern-specific and
generalizes to new words if the input is consistent and predictable. With
variable input, there is more likely to be adaptation to a general speaking
style and word-specific learning.

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

Music is a pervasive phenomenon in human culture, and musical
rhythm is virtually present in all musical traditions. Research
on the evolution and cognitive underpinnings of rhythm
can benefit from a number of approaches. We outline key concepts
and definitions, allowing fine-grained analysis of rhythmic
cognition in experimental studies. We advocate comparative
animal research as a useful approach to answer questions
about human music cognition and review experimental evidence
from different species. Finally, we suggest future directions
for research on the cognitive basis of rhythm. Apart from
research in semi-natural setups, possibly allowed by “drum set
for chimpanzees” prototypes presented here for the first time,
mathematical modeling and systematic use of circular statistics
may allow promising advances.

Abstract

Cognitive research is often focused on experimental condition-driven reactions. Ethological studies frequently
rely on the observation of naturally occurring specific behaviors. In both cases, subjects are filmed during the
study, so that afterwards behaviors can be coded on video. Coding should typically be blind to experimental
conditions, but often requires more information than that present on video. We introduce a method for blindcoding
of behavioral videos that takes care of both issues via three main innovations. First, of particular
significance for playback studies, it allows creation of a “soundtrack” of the study, that is, a track composed of
synthesized sounds representing different aspects of the experimental conditions, or other events, over time.
Second, it facilitates coding behavior using this audio track, together with the possibly muted original video.
This enables coding blindly to conditions as required, but not ignoring other relevant events. Third, our method
makes use of freely available, multi-platform software, including scripts we developed.

Abstract

The relationship between individual cognition and cultural phenomena at the society level can be transformed by cultural transmission (Kirby, Dowman, & Griffiths, 2007). Top-down models of this process have typically assumed that individuals only adopt a single linguistic trait. Recent extensions include ‘bilingual’ agents, able to adopt multiple linguistic traits (Burkett & Griffiths, 2010). However, bilingualism is more than variation within an individual: it involves the conditional use of variation with different interlocutors. That is, bilingualism is a property of a population that emerges from use. A bottom-up simulation is presented where learners are sensitive to the identity of other speakers. The simulation reveals that dynamic social structures are a key factor for the evolution of bilingualism in a population, a feature that was abstracted away in the top-down models. Top-down and bottom-up approaches may lead to different answers, but can work together to reveal and explore important features of the cultural transmission process.

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

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

We investigated the time course of sentence formulation in Tagalog, a verb-initial language in which the verb obligatorily agrees with one of its arguments. Eye-tracked participants described pictures of transitive events. Fixations to the two characters in the events were compared across sentences differing in agreement marking and post-verbal word order. Fixation patterns show evidence for two temporally dissociated phases in Tagalog sentence production. The first, driven by verb agreement, involves early linking of concepts to syntactic functions; the second, driven by word order, involves incremental lexical encoding of these concepts. These results suggest that even the earliest stages of sentence formulation may be guided by a language's grammatical structure.

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

Older listeners with high-frequency hearing loss rely more on intensity for categorisation of /s/ than normal-hearing older listeners. This study addresses the question whether this increased reliance comes about immediately when the need
arises, i.e., in the face of a spectrally-degraded signal. A phonetic categorisation task was carried out using intensitymodulated fricatives in a clean and a low-pass filtered condition with two younger and two older listener groups.
When high-frequency information was removed from the speech signal, younger listeners started using intensity as a cue. The older adults on the other hand, when presented with the low-pass filtered speech, did not rely on intensity differences for fricative identification. These results suggest that the reliance on intensity shown by the older hearingimpaired adults may have been acquired only gradually with
longer exposure to a degraded speech signal.

Abstract

In foreign-accented speech, pronunciation typically deviates from the canonical form to some degree. For native listeners, it has been shown that word recognition is more difficult for strongly-accented words than for less strongly-accented words. Furthermore recognition of strongly-accented words becomes easier with additional exposure to the foreign accent. In this paper, listeners’ behaviour was simulated with Fine-tracker, a computational model of word recognition that uses real speech as input. The simulations showed that, in line with human listeners, 1) Fine-Tracker’s recognition outcome is modulated by the degree of accentedness and 2) it improves slightly after brief exposure with the accent. On the level of individual words, however, Fine-tracker failed to correctly simulate listeners’ behaviour, possibly due to differences in overall familiarity with the chosen accent (German-accented Dutch) between human listeners and Fine-Tracker.

Abstract

Aging often affects sensitivity to the higher frequencies, which results in the loss of sensitivity to phonetic detail in speech. Hearing loss may therefore interfere with the categorisation of two consonants that have most information to differentiate between them in those higher frequencies and less in the lower frequencies, e.g., /f/ and /s/. We investigate two acoustic cues, i.e., formant transitions and fricative intensity, that older listeners might use to differentiate between /f/ and /s/. The results of two phonetic categorisation tasks on 38 older listeners (aged 60+) with varying degrees of hearing loss indicate that older listeners seem to use formant transitions as a cue to distinguish /s/ from /f/. Moreover, this ability is not impacted by hearing loss. On the other hand, listeners with increased hearing loss seem to rely more on intensity for fricative identification. Thus, progressive hearing loss may lead to gradual changes in perceptual cue weighting.

Abstract

Young listeners can quickly modify their interpretation of a speech sound when a talker produces the sound ambiguously. Young Dutch listeners rely mainly on the higher frequencies to distinguish between /f/ and /s/, but these higher frequencies are particularly vulnerable to age-related hearing loss. We therefore tested whether older Dutch listeners can show perceptual retuning given an ambiguous pronunciation in between /f/ and /s/. Results of a lexically-guided perceptual learning experiment showed that older Dutch listeners are still able to learn non-standard pronunciations of /f/ and /s/. Possibly, the older listeners have learned to rely on other acoustic cues, such as formant transitions, to distinguish between /f/ and /s/. However, the size and duration of the perceptual effect is influenced by hearing loss, with listeners with poorer hearing showing a smaller and a shorter-lived learning effect.

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

Abstract

Joint attention has long been accepted as constituting a privileged circumstance in which word learning prospers. Consequently research has investigated the role that maternal responsiveness to infant attention plays in predicting language outcomes. However there has been a recent expansion in research implicating similar predictive effects from individual differences in infant behaviours. Emerging from the foundations of such work comes an interesting question: do the relative contributions of the mother and infant to joint attention episodes impact upon language learning? In an attempt to address this, two joint attention behaviours were assessed as predictors of vocabulary attainment (as measured by OCDI Production Scores). These predictors were: mothers encouraging attention to an object given their infant was already attending to an object (maternal follow-in); and infants looking to an object given their mothers encouragement of attention to an object (infant follow-in). In a sample of 14-month old children (N=36) we compared the predictive power of these maternal and infant follow-in variables on concurrent and later language performance. Results using Growth Curve Analysis provided evidence that while both maternal follow-in and infant follow-in variables contributed to production scores, infant follow-in was a stronger predictor. Consequently it does appear to matter whose final contribution establishes joint attention episodes. Infants who more often follow-in into their mothers’ encouragement of attention have larger, and faster growing vocabularies between 14 and 18-months of age.

Abstract

Speakers sometimes modify their gestures during the process of production into adaptors such as hair touching or eye scratching. Such disguised adaptors are evidence that the speaker can monitor their gestures. In this study, we investigated when and how disguised adaptors are first produced by children. Sixty elementary school children participated in this study (ten children in each age group; from 7 to 12 years old). They were instructed to watch a cartoon and retell it to their parents. The results showed that children did not produce disguised adaptors until the age of 8. The disguised adaptors accompany fluent speech until the children are 10 years old and accompany dysfluent speech until they reach 11 or 12 years of age. These results suggest that children start to monitor their gestures when they are 9 or 10 years old. Cognitive changes were considered as factors to influence emergence of disguised adaptors

Abstract

When perceiving vowels, listeners adjust to speaker-specific vocal-tract characteristics (such as F1) through "extrinsic vowel normalization". This effect is observed as a shift in the location of categorization boundaries of vowel continua. Similar effects have been found with non-speech. Non-speech materials, however, have consistently led to smaller effect-sizes, perhaps because of a lack of attention to non-speech. The present study investigated this possibility. Non-speech materials that had previously been shown to elicit reduced normalization effects were tested again, with the addition of an attention manipulation. The results show that increased attention does not lead to increased normalization effects, suggesting that vowel normalization is mainly determined by bottom-up signal characteristics.

Abstract

ELAN is a versatile multimedia annotation tool that is being developed at the Max Planck Institute for Psycholinguistics. About a decade ago it emerged out of a number of corpus tools and utilities and it has been extended ever since. This paper focuses on the efforts made to ensure that the application keeps up with the growing needs of that era in linguistics and multimodality research; growing needs in terms of length and resolution of recordings, the number of recordings made and transcribed and the number of levels of annotation per transcription.

Abstract

Turn-taking in conversation is a cognitively demanding process that proceeds rapidly due to interlocutors utilizing a range of cues
to aid prediction. In the present study we set out to test recent claims that content question words (also called wh-words) sound similar within languages as an adaptation to help listeners predict
that a question is about to be asked. We test whether upcoming questions can be predicted based on the first phoneme of a turn and the prior context. We analyze the Switchboard corpus of English
by means of a decision tree to test whether /w/ and /h/ are good statistical cues of upcoming questions in conversation. Based on the results, we perform a controlled experiment to test whether
people really use these cues to recognize questions. In both studies
we show that both the initial phoneme and the sequential context help predict questions. This contributes converging evidence that elements of languages adapt to pragmatic pressures applied during
conversation.

Abstract

Recent empirical evidence suggests that language-mediated eye gaze is partly determined by level of formal literacy training. Huettig, Singh and Mishra (2011) showed that high-literate individuals' eye gaze was closely time locked to phonological overlap between a spoken target word and items presented in a visual display. In contrast, low-literate individuals' eye gaze was not related to phonological overlap, but was instead strongly influenced by semantic relationships between items. Our present study tests the hypothesis that this behavior is an emergent property of an increased ability to extract phonological structure from the speech signal, as in the case of high-literates, with low-literates more reliant on more coarse grained structure. This hypothesis was tested using a neural network model, that integrates linguistic information extracted from the speech signal with visual and semantic information within a central resource. We demonstrate that contrasts in fixation behavior similar to those observed between high and low literates emerge when models are trained on speech signals of contrasting granularity.

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

One of the most pressing issues in dis-
continuous constituency transition-based
parsing is that the relevant information for
parsing decisions could be located in any
part of the stack or the buffer. In this pa-
per, we propose a solution to this prob-
lem by replacing the structured percep-
tron model with a recursive neural model
that computes a global representation of
the configuration, therefore allowing even
the most remote parts of the configura-
tion to influence the parsing decisions. We
also provide a detailed analysis of how
this representation should be built out of
sub-representations of its core elements
(words, trees and stack). Additionally, we
investigate how different types of swap or-
acles influence the results. Our model is
the first neural discontinuous constituency
parser, and it outperforms all the previ-
ously published models on three out of
four datasets while on the fourth it obtains
second place by a tiny difference.

Abstract

Within scientific institutes there exist many language resources. These resources are often quite specialized and relatively unknown. The current infrastructural initiatives try to tackle this issue by collecting metadata about the resources and establishing centers with stable repositories to ensure the availability of the resources. It would be beneficial if the researcher could, by means of a simple query, determine which resources and which centers contain information useful to his or her research, or even work on a set of distributed resources as a virtual corpus. In this article we propose an architecture for a distributed search environment allowing researchers to perform searches in a set of distributed language resources.

Abstract

Motivated form-meaning mappings are pervasive in sign languages, and iconicity has recently been shown to facilitate sign learning from early on. This study investigated the role of iconicity for language acquisition in Turkish Sign Language (TID). Participants were 43 signing children (aged 10 to 45 months) of deaf parents. Sign production ability was recorded using the adapted version of MacArthur Bates Communicative Developmental Inventory (CDI) consisting of 500 items for TID. Iconicity and familiarity ratings for a subset of 104 signs were available. Our results revealed that the iconicity of a sign was positively correlated with the percentage of children producing a sign and that iconicity significantly predicted the percentage of children producing a sign, independent of familiarity or phonological complexity. Our results are consistent with previous findings on sign language acquisition and provide further support for the facilitating effect of iconic form-meaning mappings in sign learning.

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

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

Classical phonetic studies have shown that acoustic-articulatory cues can be interchanged without affecting the resulting phoneme percept (‘cue trading’). Cue trading has so far mainly been investigated in the context of phoneme identification. In this study, we investigate cue trading in word recognition, because words are the units of speech through which we communicate. This paper aims to provide a method to quantify cue trading effects by using a computational model of human word recognition. This model takes the acoustic signal as input and represents speech using articulatory feature streams. Importantly, it allows cue trading and underspecification. Its set-up is inspired by the functionality of Fine-Tracker, a recent computational model of human word recognition. This approach makes it possible, for the first time, to quantify cue trading in terms of a trade-off between features and to investigate cue trading in the context of a word recognition task.

Abstract

This paper investigates the processes in comprehending spoken noun-noun compounds, using data from the BALDEY database. BALDEY contains lexicality judgments and reaction times (RTs) for Dutch stimuli for which also linguistic information is included. Two different approaches are combined. The first is based on regression by Dynamic Survival Analysis, which models decisions and RTs as a consequence of the fact that a cumulative density function exceeds some threshold. The parameters of that function are estimated from the observed RT data. The second approach is based on DIANA, a process-oriented computational model of human word comprehension, which simulates the comprehension process with the acoustic stimulus as input. DIANA gives the identity and the number of the word candidates that are activated at each 10 ms time step.

Both approaches show how the processes involved in comprehending compounds change during a stimulus. Survival Analysis shows that the impact of word duration varies during the course of a stimulus. The density of word and non-word hypotheses in DIANA shows a corresponding pattern with different regimes. We show how the approaches complement each other, and discuss additional ways in which data and process models can be combined.

Abstract

This paper describes a computational model of speech comprehension that takes the acoustic signal as input and predicts reaction times as observed in an auditory lexical decision task. By doing so, we explore a new generation of end-to-end computational models that are able to simulate the behaviour of human subjects participating in a psycholinguistic experiment. So far, nearly all computational models of speech comprehension do not start from the speech signal itself, but from abstract representations of the speech signal, while the few existing models that do start from the acoustic signal cannot directly model reaction times as obtained in comprehension experiments. The main functional components in our model are the perception stage, which is compatible with the psycholinguistic model Shortlist B and is implemented with techniques from automatic speech recognition, and the decision stage, which is based on the linear ballistic accumulation decision model. We successfully tested our model against data from 20 participants performing a largescale auditory lexical decision experiment. Analyses show that the model is a good predictor for the average judgment and reaction time for each word.

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

Abstract

Do all languages convey semantic knowledge in the same way? If language simply mirrors the structure of the world, the answer should be a qualified “yes”. If, however, languages impose structure as much as reflecting it, then even ostensibly the “same” word in different languages may mean quite different things. We provide a first pass at a large-scale quantification of cross-linguistic semantic alignment of approximately 1000 meanings in 55 languages. We find that the translation equivalents in some domains (e.g., Time, Quantity, and Kinship) exhibit high alignment across languages while the structure of other domains (e.g., Politics, Food, Emotions, and Animals) exhibits substantial cross-linguistic variability. Our measure of semantic alignment correlates with known phylogenetic distances between languages: more phylogenetically distant languages have less semantic alignment. We also find semantic alignment to correlate with cultural distances between societies speaking the languages, suggesting a rich co-adaptation of language and culture even in domains of experience that appear most constrained by the natural world

Abstract

We are often in a bilingual situation (e.g., overhearing a conversation in the train). We investigated whether first (L1) and second language (L2) phonologies are automatically activated. A masked priming paradigm was used, with Russian words as targets and either Russian or English words as primes. Event-related potentials (ERPs) were recorded while Russian (L1) – English (L2) bilinguals read aloud L1 target words (e.g. РЕЙС /reis/ ‘fl ight’) primed with either L1 (e.g. РАНА /rana/ ‘wound’) or L2 words (e.g. PACK). Target words were read faster when they were preceded by phonologically related L1 primes but not by orthographically related L2 primes. ERPs showed orthographic priming in the 125-200 ms time window. Thus, both L1 and L2 phonologies are simultaneously activated during L1 reading. The results provide support for non-selective models of bilingual reading, which assume automatic activation of the non-target language phonology even when it is not required by the task.

Abstract

In situated communication, reference to an entity in the shared visual context can be established using eitheranexpression that conveys precise (minimally specified) or redundant (over-specified) information. There is, however, along-lasting debate in psycholinguistics concerningwhether the latter hinders referential processing. We present evidence from an eyetrackingexperiment recordingfixations as well asthe Index of Cognitive Activity –a novel measure of cognitive workload –supporting the view that over-specifications facilitate processing. We further present originalevidence that, above and beyond the effect of specificity,referring expressions thatuniformly reduce referential entropyalso benefitprocessing

Abstract

Even advanced Spanish speakers of second language English tend to confuse the pronouns ‘he’ and ‘she’, often without even noticing their mistake (Lahoz, 1991). A study by AntónMéndez (2010) has indicated that a possible reason for this error is the fact that Spanish is a pro-drop language. In order to test this hypothesis, we used an extension of Dual-path (Chang, 2002), a computational cognitive model of sentence production, to simulate two models of bilingual speech production of second language English. One model had Spanish (ES) as a native language, whereas the other learned a Spanish-like language that used the pronoun at all times (non-pro-drop Spanish, NPD_ES). When tested on L2 English sentences, the bilingual pro-drop Spanish model produced significantly more gender pronoun errors, confirming that pronoun dropping could indeed be responsible for the gender confusion in natural language use as well.

Abstract

This paper represents a pilot investigation of primed accentuation patterns produced by advanced Dutch speakers of Italian as a second language (L2). Contrastive accent patterns within prepositional phrases were elicited in a semispontaneous dialogue entertained with a confederate native speaker of Italian. The aim of the analysis was to compare learner’s contrastive accentual configurations induced by the confederate speaker’s prime against those produced by Italian and Dutch natives in the same testing conditions. F0 and speech rate data were analysed by applying powerful datadriven techniques available in the Functional Data Analysis statistical framework. Results reveal different accentual configurations in L1 and L2 Italian in response to the confederate’s prime. We conclude that learner’s accentual patterns mirror those ones produced by their L1 control group (prosodic-transfer hypothesis) although the hypothesis of a transient priming effect on learners’ choice of contrastive patterns cannot be completely ruled out.

Abstract

The task of automatic playlist continuation is generating a list of recommended tracks that can be added to an existing playlist. By suggesting appropriate tracks, i. e., songs to add to a playlist, a recommender system can increase the user engagement by making playlist creation easier, as well as extending listening beyond the end of current playlist. The ACM Recommender Systems Challenge 2018 focuses on such task. Spotify released a dataset of playlists, which includes a large number of playlists and associated track listings. Given a set of playlists from which a number of tracks have been withheld, the goal is predicting the missing tracks in those playlists. We participated in the challenge as the team Unconscious Bias and, in this paper, we present our approach. We extend adversarial autoencoders to the problem of automatic playlist continuation. We show how multiple input modalities, such as the playlist titles as well as track titles, artists and albums, can be incorporated in the playlist continuation task.

Abstract

This corpus study investigates how children figure out that functional modals
like must can express various flavors of modality. We examine how modality is
expressed in speech to and by children, and find that the way speakers use
modals may obscure their polysemy. Yet, children eventually figure it out. Our
results suggest that some do before age 3. We show that while root and
epistemic flavors are not equally well-represented in the input, there are robust
correlations between flavor and aspect, which learners could exploit to discover
modal polysemy.

Abstract

Pitch, intonation, and speech rate were analyzed in a collection of everyday speech heard by one Dutch infant between the ages of six and nine months. Components of each of these variables were measured in the speech of three adult speakers (mother, father, baby-sitter) when they addressed the infant, and when they addressed another adult. The results are in line with previously reported findings which are usually based on laboratory or prearranged settings: infant-directed speech in a natural setting exhibits more pitch variation, a larger number of simple intonation contours, and slower speech rate than does adult-directed speech.

Abstract

Dutch distinguishes at least four sentence types: statements and questions, the latter type being subdivided into wh-questions (beginning with a question word), yes/no-questions (with inversion of subject and finite), and declarative questions (lexico-syntactically identical to statement). Acoustically, each of these (sub)types was found to have clearly distinct global F0-patterns, as well as a characteristic distribution of final rises [1,2]. The present paper explores the separate contribution of parameters of global downtrend and size of accent-lending pitch movements versus aspects of the terminal rise to the human identification of the four sentence (sub)types, at various positions in the time-course of the utterance. The results show that interrogativity in Dutch can be identified at an early point in the utterance. However, wh-questions are not distinct from statements.

Abstract

In this paper we present the Virtual Language Observatory (VLO), a metadata-based portal for language resources. It is completely based on the Component Metadata (CMDI) and ISOcat standards. This approach allows for the use of heterogeneous metadata schemas while maintaining the semantic compatibility. We describe the metadata harvesting process, based on OAI-PMH, and the conversion from several formats (OLAC, IMDI and the CLARIN LRT inventory) to their CMDI counterpart profiles. Then we focus on some post-processing steps to polish the harvested records. Next, the ingestion of the CMDI files into the VLO facet browser is described. We also include an overview of the changes since the first version of the VLO, based on user feedback from the CLARIN community. Finally there is an overview of additional ideas and improvements for future versions of the VLO.

Abstract

This paper presents a corpus study that investigates the co-occurrence of reduced word forms in natural speech. We extracted Dutch past participles from three different speech registers and investigated the influence of several predictor variables on the presence and duration of schwas in prefixes and /t/s in suffixes. Our results suggest that reduced word forms tend to co-occur even if we partial out the effect of speech rate. The implications of our findings for episodic and abstractionist models of lexical representation are discussed.

Abstract

Before infants become mature speakers of their native language, they must acquire a robust word-recognition system which allows them to strike the balance between allowing some variation (mood, voice, accent) and recognizing variability that potentially changes meaning (e.g. cat vs hat). The current meta-analysis quantifies how the latter, termed mispronunciation sensitivity, changes over infants’ first three years, testing competing predictions of mainstream language acquisition theories. Our results show that infants were sensitive to mispronunciations, but accepted them as labels for target objects. Interestingly, and in contrast to predictions of mainstream theories, mispronunciation sensitivity was not modulated by infant age, suggesting that a sufficiently flexible understanding of native language phonology is in place at a young age.

Abstract

Information in speech does not unfold discretely over time; perceptual cues are gradient and overlapped. However, this varies greatly across segments and environments: listeners cannot identify the affricate in /ptS/ until the frication, but information about the vowel in /li/ begins early. Unlike most prior studies, which have concentrated on subsets of language sounds, this study tests perception of every English segment in every phonetic environment, sampling perceptual identification at six points in time (13,470 stimuli/listener; 20 listeners). Results show that information about consonants after another segment is most localized for affricates (almost entirely in the release), and most gradual for voiced stops. In comparison to stressed vowels, unstressed vowels have less information spreading to
neighboring segments and are less well identified. Indeed, many vowels,
especially lax ones, are poorly identified even by the end of the following segment. This may partly reflect listeners’ familiarity with English vowels’ dialectal variability. Diphthongs and diphthongal tense vowels show the most sudden improvement in identification, similar to affricates among the consonants, suggesting that information about segments defined by acoustic change is highly localized. This large dataset provides insights into speech perception and data for probabilistic modeling of spoken word recognition.

Abstract

In the CLARIN infrastructure various national projects have started initiatives to allow users of the infrastructure to create chains or workflows of web services. The Component Metadata (CMD) core model for web services described in this paper tries to align the metadata descriptions of these various initiatives. This should allow chaining/workflow engines to find matching and invoke services. The paper describes the landscape of web services architectures and the state of the national initiatives. Based on this a CMD core model for CLARIN is proposed, which, within some limits, can be adapted to the specific needs of an initiative by the standard facilities of CMD. The paper closes with the current state and usage of the model and a look into the future.

Abstract

The ISOcat Data Category Registry contains basically a flat and easily extensible list of data category specifications. To foster reuse and standardization only very shallow relationships among data categories are stored in the registry. However, to assist crosswalks, possibly based on personal views, between various (application) domains and to overcome possible proliferation of data categories more types of ontological relationships need to be specified. RELcat is a first prototype of a Relation Registry, which allows storing arbitrary relationships. These relationships can reflect the personal view of one linguist or a larger community. The basis of the registry is a relation type taxonomy that can easily be extended. This allows on one hand to load existing sets of relations specified in, for example, an OWL (2) ontology or SKOS taxonomy. And on the other hand allows algorithms that query the registry to traverse the stored semantic network to remain ignorant of the original source vocabulary. This paper describes first experiences with RELcat and explains some initial design decisions.

Abstract

Automatic Language Identification of written texts is a well-established area of research in Computational Linguistics. State-of-the-art algorithms often rely on n-gram character models to identify the correct language of texts, with good results seen for European languages. In this paper we propose the use of a character n-gram model and a word n-gram language model for the automatic classification of two written varieties of Portuguese: European and Brazilian. Results reached 0.998 for accuracy using character 4-grams.

Abstract

Arbil is an application designed to create and manage metadata for research data and to arrange this data into a structure appropriate for archiving. The metadata is displayed in tables, which allows an overview of the metadata and the ability to populate and update many metadata sections in bulk. Both IMDI and Clarin metadata formats are supported and Arbil has been designed as a local application so that it can also be used offline, for instance in remote field sites. The metadata can be entered in any order or at any stage that the user is able; once the metadata and its data are ready for archiving and an Internet connection is available it can be exported from Arbil and in the case of IMDI it can then be transferred to the main archive via LAMUS (archive management and upload system).

Abstract

Introduction Speech research saw a dramatic change in paradigm in the 90-ies. While earlier the discussion was dominated by a phoneticians’ approach who knew about phenomena in the speech signal, the situation completely changed after stochastic machinery such as Hidden Markov Models [1] and Artificial Neural Networks [2] had been introduced. Speech processing was now dominated by a purely mathematic approach that basically ignored all existing knowledge about the speech production process and the perception mechanisms. The key was now to construct a large enough training set that would allow identifying the many free parameters of such stochastic engines. In case that the training set is representative and the annotations of the training sets are widely ‘correct’ we could assume to get a satisfyingly functioning recognizer. While the success of knowledge-based systems such as Hearsay II [3] was limited, the statistically based approach led to great improvements in recognition rates and to industrial applications.

Abstract

According to a widely-held view among various scholars, olfaction is inferior to other human senses. It is also believed by many that languages do not have words for describing smells. Data collected among the Maniq, a small population of nomadic foragers in southern Thailand, challenge the above claims and point to a great linguistic and cultural elaboration of odor. This article presents evidence of the importance of olfaction in indigenous rituals and beliefs, as well as in the lexicon. The results demonstrate the richness and complexity of the domain of smell in Maniq society and thereby challenge the universal paucity of olfactory terms and insignificance of olfaction for humans.