Publications

Abstract

Spatial terms in the world’s languages appear to reflect both universal conceptual tendencies and linguistic convention. A similarly mixed picture in the case of color naming has been accounted for in terms of near-optimal partitions of color space. Here, we demonstrate that this account generalizes to spatial terms. We show that the spatial terms of 9 diverse languages near-optimally partition a similarity space of spatial meanings, just as color terms near-optimally partition color space. This account accommodates both universal tendencies and cross-language differences in spatial category extension, and identifies general structuring principles that appear to operate across different semantic domains.

Abstract

The previous literature has suggested that the hand movement in co-speech gestures and signs consists of a series of phases with qualitatively different dynamic characteristics. In this paper, we propose a syntagmatic rule system for movement phases that applies to both co-speech gestures and signs. Descriptive criteria for the rule system were developed for the analysis video-recorded continuous production of signs and gesture. It involves segmenting a stream of body movement into phases and identifying different phase types. Two human coders used the criteria to analyze signs and cospeech gestures that are produced in natural discourse. It was found that the criteria yielded good inter-coder reliability. These criteria can be used for the technology of automatic recognition of signs and co-speech gestures in order to segment continuous production and identify the potentially meaningbearing phase.

Abstract

The Max Planck Institute for Psycholinguistics (MPI) manages an archive of linguistic research data with a current size of almost 20 Terabytes. Apart from in-house researchers other projects also store their data in the archive, most notably the Documentation of Endangered Languages (DoBeS) projects. The archive is available online and can be accessed by anybody with Internet access. To be able to manage this large amount of data the MPI's technical group has developed a software suite called Language Archiving Technology (LAT) that on the one hand helps researchers and archive managers to manage the data and on the other hand helps users in enriching their primary data with additional layers. All the MPI software is Java-based and developed according to open source principles (GNU, 2007). All three major operating systems (Windows, Linux, MacOS) are supported and the software works similarly on all of them. As the archive is online, many of the tools, especially the ones for accessing the data, are browser based. Some of these browser-based tools make use of Adobe Flex to create nice-looking GUIs. The LAT suite is a complete set of management and enrichment tools, and given the interaction between the tools the result is a complete LAT software domain. Over the last 10 years, this domain has proven its functionality and use, and is being deployed to servers in other institutions. This deployment is an important step in getting the archived resources back to the members of the speech communities whose languages are documented. In the paper we give an overview of the tools of the LAT suite and we describe their functionality and role in the integrated process of archiving, management and enrichment of linguistic data.

Abstract

Individual and interactive movement behaviour (non-verbal behaviour / communication) specifically reflects implicit processes in psychotherapy [1,4,11]. However, thus far, the registration of movement behaviour has been a methodological challenge. We will present a coding system combined with an annotation tool for the analysis of movement behaviour during psychotherapy interviews [9]. The NGCS coding system enables to classify body movements based on their kinetic features alone [5,7]. The theoretical assumption behind the NGCS is that its main kinetic and functional movement categories are differentially associated with specific psychological functions and thus, have different neurobiological correlates [5-8]. ELAN is a multimodal annotation tool for digital video media [2,3,12]. The NGCS / ELAN template enables to link any movie to the same coding system and to have different raters independently work on the same file. The potential of movement behaviour analysis as an objective tool for psychotherapy research and for supervision in the psychosomatic practice is discussed by giving examples of the NGCS/ELAN analyses of psychotherapy sessions. While the quality of kinetic turn-taking and the therapistrsquor;s (implicit) adoption of the patientrsquor;s movements may predict therapy outcome, changes in the patientrsquor;s movement behaviour pattern may indicate changes in cognitive concepts and emotional states and thus, may help to identify therapeutically relevant processes [10].

Abstract

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

Abstract

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

Abstract

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

This paper presents a novel model for 3D image segmentation and reconstruction. It has been designed with the aim to be implemented over a computer cluster or a multi-core platform. The required features include a nearly absolute independence between the processes participating in the segmentation task and providing amount of work as equal as possible for all the participants. As a result, it is avoid many drawbacks often encountered when performing a parallelization of an algorithm that was constructed to operate in a sequential manner. Furthermore, the proposed algorithm based on the new segmentation model is efficient and shows a very good, nearly linear performance growth along with the growing number of processing units.

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

Deformable models are a popular family of image segmentation techniques, which has been gaining significant focus in the last two decades, serving both for real-world applications as well as the base for research work. One of the features that the deformable models offer and that is considered a much desired one, is the ability to change their topology during the segmentation process. Using this characteristic it is possible to perform segmentation of objects with discontinuities in their bodies or to detect an undefined number of objects in the scene. In this paper we present our model for handling the topology changes in image segmentation methods based on the Active Volumes solution. The said model is capable of performing the changes in the structure of objects while the segmentation progresses, what makes it efficient and suitable for implementations over powerful execution environment, like multi-core architectures or computer clusters.

Abstract

In this paper we present a novel approach for image segmentation using Active Nets and Active Volumes. Those solutions are based on the Deformable Models, with slight difference in the method for describing the shapes of interests - instead of using a contour or a surface they represented the segmented objects with a mesh structure, which allows to describe not only the surface of the objects but also to model their interiors. This is obtained by dividing the nodes of the mesh in two categories, namely internal and external ones, which will be responsible for two different tasks. In our new approach we propose to negate this separation and use only one type of nodes. Using that assumption we manage to significantly shorten the time of segmentation while maintaining its quality.

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

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

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

The goal of the presented study was to investigate the use of coarticulatory vowel nasalization in lexical access by native speakers of American English. In particular, we compare the use of coart culatory place of articulation cues to that of coarticulatory vowel nasalization. Previous research on lexical access has shown that listeners use cues to the place of articulation of a postvocalic stop in the preceding vowel. However, vowel nasalization as cue to an upcoming nasal consonant has been argued to be a more complex phenomenon. In order to establish whether coarticulatory vowel nasalization aides in the process of lexical access in the same way as place of articulation cues do, we conducted two perception experiments: an off-line 2AFC discrimination task and an on-line eyetracking study using the visual world paradigm. The results of our study suggest that listeners are indeed able to use vowel nasalization in similar ways to place of articulation information, and that both types of cues aide in lexical access.

Abstract

The results of a word-spotting experiment are presented in which Dutch listeners tried to spot different types of bisyllabic Dutch words embedded in different types of nonsense contexts. Embedded verbs were not reliably harder to spot than embedded nouns; this suggests that nouns and verbs are recognised via the same basic processes. Iambic words were no harder to spot than trochaic words, suggesting that trochaic words are not in principle easier to recognise than iambic words. Words were harder to spot in consonantal contexts (i.e., contexts which themselves could not be words) than in longer contexts which contained at least one vowel (i.e., contexts which, though not words, were possible words of Dutch). A control experiment showed that this difference was not due to acoustic differences between the words in each context. The results support the claim that spoken-word recognition is sensitive to the viability of sound sequences as possible words.

Abstract

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

Abstract

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

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

Corpus linguistics and language documentation are usually considered separate subdisciplines within linguistics, having developed from different traditions and often operating on different scales, but the authors will suggest that there are commonalities to the two: both aim to represent language use in a community, and both are concerned with managing digital data. The authors propose that the development of the Australian National Corpus (AusNC) be guided by the experience of language documentation in the management of multimodal digital data and its annotation, and in ethical issues pertaining to making the data accessible. This would allow an AusNC that is distributed, multimodal, and multilingual, with holdings of text, audio, and video data distributed across multiple institutions; and including Indigenous, sign, and migrant community languages. An audit of language material held by Australian institutions and individuals is necessary to gauge the diversity and volume of possible content, and to inform common technical standards.

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

This paper describes recent experimental evidence which shows that models of spoken word recognition must incorporate both inhibition between competing lexical candidates and a sensitivity to metrical cues to lexical segmentation. A new version of the Shortlist [1][2] model incorporating the Metrical Segmentation Strategy [3] provides a detailed simulation of the data.

Abstract

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

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

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

LEXUS and ViCoS: from lexicon to conceptual spaces LEXUS is a web-based lexicon tool and the knowledge space software ViCoS is an extension of LEXUS, allowing users to create relations between objects in and across lexica. LEXUS and ViCoS are part of the Language Archiving Technology software, developed at the MPI for Psycholinguistics to archive and enrich linguistic resources collected in the framework of language documentation projects. LEXUS is of primary interest for language documentation, offering the possibility to not just create a digital dictionary, but additionally it allows the creation of multi-media encyclopedic lexica. ViCoS provides an interface between the lexical space and the ontological space. Its approach permits users to model a world of concepts and their interrelations based on categorization patterns made by the speech community. We describe the LEXUS and ViCoS functionalities using three cases from DoBeS language documentation projects: (1) Marquesan The Marquesan lexicon was initially created in Toolbox and imported into LEXUS using the Toolbox import functionality. The lexicon is enriched with multi-media to illustrate the meaning of the words in its cultural environment. Members of the speech community consider words as keys to access and describe relevant parts of their life and traditions. Their understanding of words is best described by the various associations they evoke rather than in terms of any formal theory of meaning. Using ViCoS a knowledge space of related concepts is being created. (2) Kola-Sámi Two lexica are being created in LEXUS: RuSaDic lexicon is a Russian-Kildin wordlist in which the entries are of relative limited structure and content. SaRuDiC is a more complex structured lexicon with much richer content, including multi-media fragments and derivations. Using ViCoS we have created a connection between the two lexica, so that speakers who are familiair with Russian and wish to revitalize their Kildin can enter the lexicon through the RuSaDic and from there approach the informative SaRuDic. Similary we will create relations from the two lexica to external open databases, like e.g. Álgu. (3) Beaver A speaker database including kinship relations has been created and the database has been imported into LEXUS. In the LEXUS views the relations for individual speakers are being displayed. Using ViCoS the relational information from the database will be extracted to form a kisnhip relation space with specific relation types, like e.g 'mother-of'. The whole set of relations from the database can be displayed in one ViCoS relation window, and zoom functionality is available.

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

Emotional signals allow for the sharing of important information with conspecifics, for example to warn them of danger. Humans use a range of different cues to communicate to others how they feel, including facial, vocal, and gestural signals. Although much is known about facial expressions of emotion, less research has focused on affect in the voice. We compare British listeners to individuals from remote Namibian villages who have had no exposure to Western culture, and examine recognition of non-verbal emotional vocalizations, such as screams and laughs. We show that a number of emotions can be universally recognized from non-verbal vocal signals. In addition we demonstrate the specificity of this pattern, with a set of additional emotions only recognized within, but not across these cultural groups. Our findings indicate that a small set of primarily negative emotions have evolved signals across several modalities, while most positive emotions are communicated with culture-specific 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

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

A stretch of speech is often consistent with multiple words, e.g., the sequence /hæm/ is consistent with ‘ham’ but also with the first syllable of ‘hamster’, resulting in temporary ambiguity. However, to what degree does this lexical embedding occur? Analyses on two corpora of spoken Dutch showed that 11.9%-19.5% of polysyllabic word tokens have word-initial embedding, while 4.1%-7.5% of monosyllabic word tokens can appear word-initially embedded. This is much lower than suggested by an analysis of a large dictionary of Dutch. Speech processing thus appears to be simpler than one might expect on the basis of statistics on a dictionary.

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

Evidence that listeners use durational cues to help resolve temporarily ambiguous speech input has accumulated over the past few years. In this paper, we investigate whether durational cues are also beneficial for word recognition in a computational model of spoken-word recognition. Two sets of simulations were carried out using the acoustic signal as input. The simulations showed that the computational model, like humans, takes benefit from durational cues during word recognition, and uses these to disambiguate the speech signal. These results thus provide support for the theory that durational cues play a role in spoken-word recognition.

Abstract

This paper presents a study on the reduction of word-final [t]s in conversational standard Dutch. Based on a large amount of tokens annotated on the segmental level, we show that the bigram frequency and the segmental context are the main predictors for the absence of [t]s. In a second study, we present an analysis of the detailed acoustic properties of word-final [t]s and we show that bigram frequency and context also play a role on the subsegmental level. This paper extends research on the realization of /t/ in spontaneous speech and shows the importance of incorporating sub-segmental properties in models of speech.

Abstract

The present paper is part of a large research programme investigating the nature and properties of the predicate logic inherent in natural language. The general hypothesis is that natural speakers start off with a basic-natural logic, based on natural cognitive functions, including the basic-natural way of dealing with plural objects. As culture spreads, functional pressure leads to greater generalization and mathematical correctness, yielding ever more refined systems until the apogee of standard modern predicate logic. Four systems of predicate calculus are considered: Basic-Natural Predicate Calculus (BNPC), Aritsotelian-Abelardian Predicate Calculus (AAPC), Aritsotelian-Boethian Predicate Calculus (ABPC), also known as the classic Square of Opposition, and Standard Modern Predicate Calculus (SMPC). (ABPC is logically faulty owing to its Undue Existential Import (UEI), but that fault is repaired by the addition of a presuppositional component to the logic.) All four systems are checked against seven natural logical intuitions. It appears that BNPC scores best (five out of seven), followed by ABPC (three out of seven). AAPC and SMPC finish ex aequo with two out of seven.

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

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

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

The problem of identifying and correcting confusibles, i.e. context-sensitive spelling errors, in text is typically tackled using specifically trained machine learning classifiers. For each different set of confusibles, a specific classifier is trained and tuned. In this research, we investigate a more generic approach to context-sensitive confusible correction. Instead of using specific classifiers, we use one generic classifier based on a language model. This measures the likelihood of sentences with different possible solutions of a confusible in place. The advantage of this approach is that all confusible sets are handled by a single model. Preliminary results show that the performance of the generic classifier approach is only slightly worse that that of the specific classifier approach

Abstract

In the context of confusible disambiguation (spelling correction that requires context), the synchronous back-off strategy combined with traditional n-gram language models performs well. However, when alternatives consist of a different number of tokens, this classification technique cannot be applied directly, because the computation of the probabilities is skewed. Previous work already showed that probabilities based on different order n-grams should not be compared directly. In this article, we propose new probability metrics in which the size of the n is varied according to the number of tokens of the confusible alternative. This requires access to n-grams of variable length. Results show that the synchronous back-off method is extremely robust. We discuss the use of suffix trees as a technique to store variable length n-gram information efficiently.

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

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

The present study shows that [t] consonants are affected by probabilistic factors in a syllable-timed language as French, and in spontaneous as well as in journalistic speech. Study 1 showed a word bigram frequency effect in spontaneous French, but its exact nature depended on the corpus on which the probabilistic measures were based. Study 2 investigated journalistic speech and showed an effect of the joint frequency of the test word and its following word. We discuss the possibility that these probabilistic effects are due to the speaker’s planning of upcoming words, and to the speaker’s adaptation to the listener’s needs.

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

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

A dominant hypothesis in empirical research on the evolution of language is the following: the fundamental difference between animal and human communication systems is captured by the distinction between regular and more complex non-regular grammars. Studies reporting successful artificial grammar learning of nested recursive structures and imaging studies of the same have methodological shortcomings since they typically allow explicit problem solving strategies and this has been shown to account for the learning effect in subsequent behavioral studies. The present study overcomes these shortcomings by using subtle violations of agreement structure in a preference classification task. In contrast to the studies conducted so far, we use an implicit learning paradigm, allowing the time needed for both abstraction processes and consolidation to take place. Our results demonstrate robust implicit learning of recursively embedded structures (context-free grammar) and recursive structures with cross-dependencies (context-sensitive grammar) in an artificial grammar learning task spanning 9 days. Keywords: Implicit artificial grammar learning; centre embedded; cross-dependency; implicit learning; context-sensitive grammar; context-free grammar; regular grammar; non-regular grammar

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 paper describes a new flexible delexicalization method based on glottal excited parametric speech synthesis scheme. The system utilizes inverse filtered glottal flow and all-pole modelling of the vocal tract. The method provides a possibility to retain and manipulate all relevant prosodic features of any kind of speech. Most importantly, the features include voice quality, which has not been properly modeled in earlier delexicalization methods. The functionality of the new method was tested in a prosodic tagging experiment aimed at providing word prominence data for a text-to-speech synthesis system. The experiment confirmed the usefulness of the method and further corroborated earlier evidence that linguistic factors influence the perception of prosodic prominence.

Abstract

A not uncommon assumption in psycholinguistics is that the N400 directly indexes high-level semantic integration, the compositional, word-driven construction of sentence- and discourse-level meaning in some language-relevant unification space. The various discourse- and speaker-dependent modulations of the N400 uncovered by us and others are often taken to support this 'compositional integration' position. In my talk, I will argue that these N400 modulations are probably better interpreted as only indirectly reflecting compositional sense-making. The account that I will advance for these N400 effects is a variant of the classic Kutas and Federmeier (2002, TICS) memory retrieval account in which context effects on the word-elicited N400 are taken to reflect contextual priming of LTM access. It differs from the latter in making more explicit that the contextual cues that prime access to a word's meaning in LTM can range from very simple (e.g., a single concept) to very complex ones (e.g., a structured representation of the current discourse). Furthermore, it incorporates the possibility, suggested by recent N400 findings, that semantic retrieval can also be intensified in response to certain ‘relevance signals’, such as strong value-relevance, or a marked delivery (linguistic focus, uncommon choice of words, etc). In all, the perspective I'll draw is that in the context of discourse-level language processing, N400 effects reflect an 'overlay of technologies', with the construction of discourse-level representations riding on top of more ancient sense-making technology.

Abstract

This paper reports two experiments with vowels and consonants as phoneme detection targets in real words. In the first experiment, two relatively distinct vowels were compared with two confusible stop consonants. Response times to the vowels were longer than to the consonants. Response times correlated negatively with target phoneme length. In the second, two relatively distinct vowels were compared with their corresponding semivowels. This time, the vowels were detected faster than the semivowels. We conclude that response time differences between vowels and stop consonants in this task may reflect differences between phoneme categories in the variability of tokens, both in the acoustic realisation of targets and in the' representation of targets by subjects.

Abstract

Listeners require context to understand the casual pronunciation variants of words that are typical of spontaneous speech (Ernestus et al., 2002). The present study reports two auditory lexical decision experiments, investigating listeners' use of semantic contextual information in the comprehension of unreduced and reduced words. We found a strong semantic priming effect for low frequency unreduced words, whereas there was no such effect for reduced words. Word frequency was facilitatory for all words. These results show that semantic context is relevant especially for the comprehension of unreduced words, which is unexpected given the listener driven explanation of reduction in spontaneous speech.

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.