Publications

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

Despite differences in language, culture, and ecology, some human characteristics are similar in people all over the world, while other features vary from one group to the next. These similarities and differences can inform arguments about what aspects of the human mind are part of our shared biological heritage and which are predominantly products of culture and language. I will present data from a cross-cultural project investigating the recognition of non-verbal vocalizations of emotions, such as screams and laughs, across two highly different cultural groups. English participants were compared to individuals from remote, culturally isolated Namibian villages. Vocalizations communicating the so-called “basic emotions” (anger, disgust, fear, joy, sadness, and surprise) were bidirectionally recognised. In contrast, a set of additional positive emotions was only recognised within, but not across, cultural boundaries. These results indicate that a number of primarily negative emotions are associated with vocalizations that can be recognised across cultures, while at least some positive emotions are communicated with culture-specific signals. I will discuss these findings in the context of accounts of emotions at differing levels of analysis, with an emphasis on the often-neglected positive emotions.

Abstract

Many studies suggest that emotional signals can be recognized across cultures and modalities. But to what extent are these signals innate and to what extent are they learned? This study investigated whether auditory learning is necessary for the production of recognizable emotional vocalizations by examining the vocalizations produced by people born deaf. Recordings were made of eight congenitally deaf Dutch individuals, who produced non-verbal vocalizations of a range of negative and positive emotions. Perception was examined in a forced-choice task with hearing Dutch listeners (n = 25). Considerable variability was found across emotions, suggesting that auditory learning is more important for the acquisition of certain types of vocalizations than for others. In particular, achievement and surprise sounds were relatively poorly recognized. In contrast, amusement and disgust vocalizations were well recognized, suggesting that for some emotions, recognizable vocalizations can develop without any auditory learning. The implications of these results for models of emotional communication are discussed, and other routes of social learning available to the deaf individuals are considered.

Abstract

Vocalizations like screams and laughs are used to communicate affective states, but what acoustic cues in these signals require vocal learning and which ones are innate? This study investigated the role of auditory learning in the production of non-verbal emotional vocalizations by examining the vocalizations produced by people born deaf. Recordings were made of congenitally deaf Dutch individuals and matched hearing controls, who produced non-verbal vocalizations of a range of negative and positive emotions. Perception was examined in a forced-choice task with hearing Dutch listeners (n = 25), and judgments were analyzed together with acoustic cues, including envelope, pitch, and spectral measures. Considerable variability was found across emotions and acoustic cues, and the two types of information were related for a sub-set of the emotion categories. These results suggest that auditory learning is less important for the acquisition of certain types of vocalizations than for others (particularly amusement and relief), and they also point to a less central role for auditory learning of some acoustic features in affective non-verbal vocalizations. The implications of these results for models of vocal emotional communication are discussed.

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

This paper presents a study on the acoustic sub-segmental properties of word-final /t/ in conversational standard Dutch and how these properties contribute to whether humans and an ASR system classify the /t/ as acoustically present or absent. In general, humans and the ASR system use the same cues (presence of a constriction, a burst, and alveolar frication), but the ASR system is also less sensitive to fine cues (weak bursts, smoothly starting friction) than human listeners and misled by the presence of glottal vibration. These data inform the further development of models of human and automatic speech processing.

Abstract

Spontal-N is a corpus of spontaneous, interactional Norwegian. To our knowledge, it is the first corpus of Norwegian in which the majority of speakers have spent significant parts of their lives in Sweden, and in which the recorded speech displays varying degrees of interference from Swedish. The corpus consists of studio quality audio- and video-recordings of four 30-minute free conversations between acquaintances, and a manual orthographic transcription of the entire material. On basis of the orthographic transcriptions, we automatically annotated approximately 50 percent of the material on the phoneme level, by means of a forced alignment between the acoustic signal and pronunciations listed in a dictionary. Approximately seven percent of the automatic transcription was manually corrected. Taking the manual correction as a gold standard, we evaluated several sources of pronunciation variants for the automatic transcription. Spontal-N is intended as a general purpose speech resource that is also suitable for investigating phonetic detail.

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

This paper presents the Kachna corpus of spontaneous speech, in which ten Czech and ten Norwegian speakers were recorded both in their native language and in English. The dialogues are elicited using a picture replication task that requires active cooperation and interaction of speakers by asking them to produce a drawing as close to the original as possible. The corpus is appropriate for the study of interactional features and speech reduction phenomena across native and second languages. The combination of productions in non-native English and in speakers’ native language is advantageous for investigation of L2 issues while providing a L1 behaviour reference from all the speakers. The corpus consists of 20 dialogues comprising 12 hours 53 minutes of recording, and was collected in 2008. Preparation of the transcriptions, including a manual orthographic transcription and an automatically generated phonetic transcription, is currently in progress. The phonetic transcriptions are automatically generated by aligning acoustic models with the speech signal on the basis of the orthographic transcriptions and a dictionary of pronunciation variants compiled for the relevant language. Upon completion the corpus will be made available via the European Language Resources Association (ELRA).

Abstract

Why do people accommodate to each other’s linguistic behavior? Studies of natural interactions (Giles, Taylor & Bourhis, 1973) suggest that speakers accommodate to achieve interactional goals, influencing what their interlocutor thinks or feels about them. But is this the only reason speakers accommodate? In real-world conversations, interactional motivations are ubiquitous, making it difficult to assess the extent to which they drive accommodation. Do speakers still accommodate even when interactional goals cannot be achieved, for instance, when their interlocutor cannot interpret their accommodation behavior? To find out, we asked participants to enter an immersive virtual reality (VR) environment and to converse with a virtual interlocutor. Participants accommodated to the speech rate of their virtual interlocutor even though he could not interpret their linguistic behavior, and thus accommodation could not possibly help them to achieve interactional goals. Results show that accommodation does not require explicit interactional goals, and suggest other social motivations for accommodation.

Abstract

In this article, we propose the use of suffix arrays to efficiently implement n-gram language models with practically unlimited size n. This approach, which is used with synchronous back-off, allows us to distinguish between alternative sequences using large contexts. We also show that we can build this kind of models with additional information for each symbol, such as part-of-speech tags and dependency information. The approach can also be viewed as a collection of virtual k-testable automata. Once built, we can directly access the results of any k-testable automaton generated from the input training data. Synchronous back- off automatically identies the k-testable automaton with the largest feasible k. We have used this approach in several classification tasks.

Abstract

Finding regularities in large data sets requires implementations of systems that are efficient in both time and space requirements. Here, we describe a newly developed system that exploits the internal structure of the enhanced suffixarray to find significant patterns in a large collection of sequences. The system searches exhaustively for all significantly compressing patterns where patterns may consist of symbols and skips or wildcards. We demonstrate a possible application of the system by detecting interesting patterns in a Dutch and an English corpus.

Abstract

In this article, we propose the use of suffix arrays to implement n-gram language models with practically unlimited size n. These unbounded n-grams are called 1-grams. This approach allows us to use large contexts efficiently to distinguish between different alternative sequences while applying synchronous back-off. From a practical point of view, the approach has been applied within the context of spelling confusibles, verb and noun agreement and prenominal adjective ordering. These initial experiments show promising results and we relate the performance to the size of the n-grams used for disambiguation.

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

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

This study investigates phrase-medial vowel devoicing in European French (e.g. /ty po/ [typo] 'you can'). Our spontaneous speech data confirm that French phrase-medial devoicing is a frequent phenomenon affecting high vowels preceded by voiceless consonants. We also found that devoicing is more frequent in temporally reduced and coarticulated vowels. Complete and partial devoicing were conditioned by the same variables (speech rate, consonant type and distance from the end of the AP). Given these results, we propose that phrase-medial vowel devoicing in French arises mainly from the temporal compression of vocalic gestures and the aerodynamic conditions imposed by high vowels.

Abstract

This article describes the preparation, recording and orthographic transcription of a new speech corpus, the Nijmegen Corpus of Casual Spanish (NCCSp). The corpus contains around 30 hours of recordings of 52 Madrid Spanish speakers engaged in conversations with friends. Casual speech was elicited during three different parts, which together provided around ninety minutes of speech from every group of speakers. While Parts 1 and 2 did not require participants to perform any specific task, in Part 3 participants negotiated a common answer to general questions about society. Information about how to obtain a copy of the corpus can be found online at http://mirjamernestus.ruhosting.nl/Ernestus/NCCSp

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

Every language manifests casual speech processes, and hence every second language too. This study examined how listeners deal with second-language casual speech processes, as a function of the processes in their native language. We compared a match case, where a second-language process t/-reduction) is also operative in native speech, with a mismatch case, where a second-language process (/r/-insertion) is absent from native speech. In each case native and non-native listeners judged stimuli in which a given phoneme (in sentence context) varied along a continuum from absent to present. Second-language listeners in general mimicked native performance in the match case, but deviated significantly from native performance in the mismatch case. Together these results make it clear that the mapping from first to second language is as important in the interpretation of casual speech processes as in other dimensions of speech perception. Unfamiliar casual speech processes are difficult to adapt to in a second language. Casual speech processes that are already familiar from native speech, however, are easy to adapt to; indeed, our results even suggest that it is possible for subtle difference in their occurrence patterns across the two languages to be detected,and to be accommodated to in second-language listening.

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

Controlled vocabularies of various kinds (e.g., thesauri, classification schemes) play an integral part in making Cultural Heritage collections accessible. The various institutions participating in the Dutch CATCH programme maintain and make use of a rich and diverse set of vocabularies. This makes it hard to provide a uniform point of access to all collections at once. Our SKOS-based vocabulary and alignment repository aims at providing technology for managing the various vocabularies, and for exploiting semantic alignments across any two of them. The repository system exposes web services that effectively support the construction of tools for searching and browsing across vocabularies and collections or for collection curation (indexing), as we demonstrate.

Abstract

Multivariate analysis allows decoding of single trial data in individual subjects. Since different models are obtained for each subject it becomes hard to perform an analysis on the group level. We introduce a new algorithm for Bayesian multi-task learning which imposes a coupling between single-subject models. Using
the CMU fMRI dataset it is shown that the algorithm can be used for concept classification
based on the average activation of regions in the AAL atlas. Concepts which were most easily classified correspond to the categories shelter,manipulation and eating, which is in accordance with the literature. The multi-task learning algorithm is shown to find regions of interest that are common to all subjects which
therefore facilitates interpretation of the obtained
models.

Abstract

Previous research suggests that bilinguals presented with low and high predictability sentences benefit from semantics in clear but not in conversational speech [1]. In everyday speech, however, many words are not highly predictable. Previous research has shown that native listeners can use also more subtle semantic contextual information [2]. The present study reports two auditory lexical decision experiments investigating to what extent late Asian-English bilinguals benefit from subtle semantic cues in their processing of English unreduced and reduced speech. Our results indicate that these bilinguals are less sensitive to semantic cues than native listeners for both speech registers.

Abstract

Over the years, the field of Language Resources and Technology (LRT) hasdeveloped a tremendous amount of resources and tools. However, there is noready-to-use map that researchers could use to gain a good overview andsteadfast orientation when searching for, say corpora or software tools tosupport their studies. It is rather the case that information is scatteredacross project- or organisation-specific sites, which makes it hard if notimpossible for less-experienced researchers to gather all relevant material.Clearly, the provision of metadata is central to resource and softwareexploration. However, in the LRT field, metadata comes in many forms, tastesand qualities, and therefore substantial harmonization and curation efforts arerequired to provide researchers with metadata-based guidance. To address thisissue a broad alliance of LRT providers (CLARIN, the Linguist List, DOBES,DELAMAN, DFKI, ELRA) have initiated the Virtual Language Observatory portal toprovide a low-barrier, easy-to-follow entry point to language resources andtools; it can be accessed via http://www.clarin.eu/vlo

Abstract

This paper presents an analysis of phoneme durations of emotional speech in two languages: Dutch and Korean. The analyzed corpus of emotional speech has been specifically developed for the purpose of cross-linguistic comparison, and is more balanced than any similar corpus available so far: a) it contains expressions by both Dutch and Korean actors and is based on judgments by both Dutch and Korean listeners; b) the same elicitation technique and recording procedure were used for recordings of both languages; and c) the phonetics of the carrier phrase were constructed to be permissible in both languages. The carefully controlled phonetic content of the carrier phrase allows for analysis of the role of specific phonetic features, such as phoneme duration, in emotional expression in Dutch and Korean. In this study the mutual effect of language and emotion on phoneme duration is presented.

Abstract

In this paper we investigate a computational model of word learning, that is embedded in a cognitively and ecologically plausible framework. Multi-modal stimuli from four different speakers form a varied source of experience. The model incorporates active learning, attention to a communicative setting and clarity of the visual scene. The model's ability to learn associations between speech utterances and visual concepts is evaluated during training to investigate the influence of active learning under conditions of uncertain input. The results show the importance of shared attention in word learning and the model's robustness against noise.

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

Recent studies using phoneme detection tasks have shown that spoken-language processing is neither facilitated nor interfered with by optional assimilation, but is inhibited by violation of obligatory assimilation. Interpretation of these results depends on an assessment of their generality, specifically, whether they also obtain when listeners are processing nonnative language. Two separate experiments are presented in which native listeners of German and native listeners of Dutch had to detect a target fricative in legal monosyllabic Dutch nonwords. All of the nonwords were correct realisations in standard Dutch. For German listeners, however, half of the nonwords contained phoneme strings which violate the German fricative assimilation rule. Whereas the Dutch listeners showed no significant effects, German listeners detected the target fricative faster when the German fricative assimilation was violated than when no violation occurred. The results might suggest that violation of assimilation rules does not have to make processing more difficult per se.

Abstract

In foreign-accented speech, listeners have to handle noticeable deviations from the standard pronunciation of a target language. Three cross-modal priming experiments investigated how short- and long-term experiences with a foreign accent influence word recognition by native listeners. In experiment 1, German-accented words were presented to Dutch listeners who had either extensive or limited prior experience with German-accented Dutch. Accented words either contained a diphthong substitution that deviated acoustically quite largely from the canonical form (huis [hys], "house", pronounced as [hoys]), or that deviated acoustically to a lesser extent (lijst [lst], "list", pronounced as [lst]). The mispronunciations never created lexical ambiguity in Dutch. While long-term experience facilitated word recognition for both types of substitutions, limited experience facilitated recognition only of words with acoustically smaller deviations. In experiment 2, Dutch listeners with limited experience listened to the German speaker for 4 min before participating in the cross-modal priming experiment. The results showed that speaker-specific learning effects for acoustically large deviations can be obtained already after a brief exposure, as long as the exposure contains evidence of the deviations. Experiment 3 investigates whether these short-term adaptation effects for foreign-accented speech are speaker-independent.

Abstract

In the emerging e-Science scenario users should be able to easily combine data resources and tools/services; and machines should automatically be able to trace paths and carry out interpretations. Users who want to participate need to move from a down-load first to a cyberinfrastructure paradigm, thus increasing their dependency on the seamless operation of all components in the Internet. Such a scenario is inherently complex and requires compliance to guidelines and standards to keep it working smoothly. Only a change in our culture of dealing with research data and awareness about the way we do data lifecycle management will lead to success. Since we have so many legacy resources that are not compliant with the required guidelines, since we need to admit obvious problems in particular with standardization in the area of semantics and since it will take much time to establish trust at the side of researchers, the e-Science scenario can only be achieved stepwise which will take much time.

Abstract

In this paper, we describe the core pillars of a large archive oflanguage material recorded worldwide partly about languages that are highly endangered. The bases for the documentation of these languages are audio/video recordings which are then annotated at several linguistic layers. The digital age completely changed the requirements of long-term preservation and it is discussed how the archive met these new challenges. An extensive solution for data replication has been worked out to guarantee bit-stream preservation. Due to an immediate conversion of the incoming data to standards -based formats and checks at upload time lifecycle management of all 50 Terabyte of data is widely simplified. A suitable metadata framework not only allowing users to describe and discover resources, but also allowing them to organize their resources is enabling the management of this amount of resources very efficiently. Finally, it is the Language Archiving Technology software suite which allows users to create, manipulate, access and enrich all archived resources given that they have access permissions.

Abstract

Currently, research infrastructures are being designed and established in manydisciplines since they all suffer from an enormous fragmentation of theirresources and tools. In the domain of language resources and tools the CLARINinitiative has been funded since 2008 to overcome many of the integration andinteroperability hurdles. CLARIN can build on knowledge and work from manyprojects that were carried out during the last years and wants to build stableand robust services that can be used by researchers. Here service centres willplay an important role that have the potential of being persistent and thatadhere to criteria as they have been established by CLARIN. In the last year ofthe so-called preparatory phase these centres are currently developing four usecases that can demonstrate how the various pillars CLARIN has been working oncan be integrated. All four use cases fulfil the criteria of beingcross-national.

Abstract

The amount of research data in the Humanities is increasing at fastspeed. Metadata helps describing and making accessible this data tointerested researchers within and across institutions. While metadatainteroperability is an issue that is being recognised and addressed,the systematic and user-driven provision of annotations and thelinking together of resources into new organisational layers havereceived much less attention. This paper gives an overview of ourevolving technological eScience environment to support suchfunctionality. It describes two tools, ADDIT and ViCoS, which enableresearchers, rather than archive managers, to organise and reorganiseresearch data to fit their particular needs. The two tools, which areembedded into our institute's existing software landscape, are aninitial step towards an eScience environment that gives our scientistseasy access to (multimodal) research data of their interest, andempowers them to structure, enrich, link together, and share such dataas they wish.