Publications

Abstract

Continuous vector word representations (or
word embeddings) have shown success in cap-turing semantic relations between words, as evidenced by evaluation against behavioral data of adult performance on semantic tasks (Pereira et al., 2016). Adult semantic knowl-edge is the endpoint of a language acquisition process; thus, a relevant question is whether these models can also capture emerging word
representations of young language learners. However, the data for children’s semantic knowledge across development is scarce. In this paper, we propose to bridge this gap by using Age of Acquisition norms to evaluate word embeddings learnt from child-directed input. We present two methods that evaluate word embeddings in terms of (a) the semantic neighbourhood density of learnt words, and (b) con-
vergence to adult word associations. We apply our methods to bag-of-words models, and find that (1) children acquire words with fewer semantic neighbours earlier, and (2) young learners only attend to very local context. These findings provide converging evidence for validity of our methods in understanding the prerequisite features for a distributional model of word learning.

Abstract

The term “uptalk” describes utterance-final pitch rises that carry no sentence-structural information. Uptalk is usually dialectal or sociolectal, and Australian English (AusEng) is particularly known for this attribute. We ask here whether experience with an uptalk variety affects listeners’ ability to categorise rising pitch contours on the basis of the timing and height of their onset and offset. Listeners were two groups of English-speakers (AusEng, and American English), and three groups of listeners with L2 English: one group with Mandarin as L1 and experience of listening to AusEng, one with German as L1 and experience of listening to AusEng, and one with German as L1 but no AusEng experience. They heard nouns (e.g. flower, piano) in the framework “Got a NOUN”, each ending with a pitch rise artificially manipulated on three contrasts: low vs. high rise onset, low vs. high rise offset and early vs. late rise onset. Their task was to categorise the tokens as “question” or “statement”, and we analysed the effect of the pitch contrasts on their judgements. Only the native AusEng listeners were able to use the pitch contrasts systematically in making these categorisations.

Abstract

Psycholinguistic studies of metaphor processing must control their stimuli not just for word frequency but also for the frequency with which a term is used metaphorically. Thus, we consider the task of metaphor frequency estimation, which predicts how often target words will be used metaphorically. We develop metaphor classifiers which represent metaphorical domains through Latent Dirichlet Allocation, and apply these classifiers to the target words, aggregating their decisions to estimate the metaphorical frequencies. Training on only 400 sentences, our models are able to achieve 61.3 % accuracy on metaphor classification and 77.8 % accuracy on HIGH vs. LOW metaphorical frequency estimation.

Abstract

This paper introduces the EU-FP7 project CLARIN, a joint effort of over 150 institutions in Europe, aimed at the creation of a sustainable language resources and technology infrastructure for the humanities and social sciences research community. The paper briefly introduces the vision behind the project and how it relates to speech research with a focus on the contributions that CLARIN can and will make to research in spoken language processing.

Abstract

Large auditory-visual (AV) speech corpora are the grist of modern research in speech science, but no such corpus exists for Australian English. This is unfortunate, for speech science is the brains behind speech technology and applications such as text-to-speech (TTS) synthesis, automatic speech recognition (ASR), speaker recognition and forensic identification, talking heads, and hearing prostheses. Advances in these research areas in Australia require a large corpus of Australian English. Here the authors describe a blueprint for building the Big Australian Speech Corpus (the Big ASC), a corpus of over 1,100 speakers from urban and rural Australia, including speakers of non-indigenous, indigenous, ethnocultural, and disordered forms of Australian English, each of whom would be sampled on three occasions in a range of speech tasks designed by the researchers who would be using the corpus.

Abstract

According to theories of embodied cognition, understanding a verb like throw involves unconsciously simulating the action throwing, using areas of the brain that support motor planning. If understanding action words involves mentally simulating our own actions, then the neurocognitive representation of word meanings should differ for people with different kinds of bodies, who perform actions in systematically different ways. In a test of the body-specificity hypothesis (Casasanto, 2009), we used fMRI to compare premotor activity correlated with action verb understanding in right- and left-handers. Right-handers preferentially activated left premotor cortex during lexical decision on manual action verbs (compared with non-manual action verbs), whereas left-handers preferentially activated right premotor areas. This finding helps refine theories of embodied semantics, suggesting that implicit mental simulation during language processing is body-specific: Right and left-handers, who perform actions differently, use correspondingly different areas of the brain for representing action verb meanings.

Abstract

What is the relationship between motor action and emotion? Here we investigated whether people associate good things more strongly with the dominant side of their bodies, and bad things with the non-dominant side. To find out, we analyzed spontaneous gestures during speech expressing ideas with positive or negative emotional valence (e.g., freedom, pain, compassion). Samples of speech and gesture were drawn from the 2004 and 2008 US presidential debates, which involved two left-handers (Obama, McCain) and two right-handers (Kerry, Bush). Results showed a strong association between the valence of spoken clauses and the hands used to make spontaneous co-speech gestures. In right-handed candidates, right-hand gestures were more strongly associated with positive-valence clauses, and left-hand gestures with negative-valence clauses. Left-handed candidates showed the opposite pattern. Right- and left-handers implicitly associated positive valence more strongly with their dominant hand: the hand they can use more fluently. These results support the body-specificity hypothesis, (Casasanto, 2009), and suggest a perceptuomotor basis for even our most abstract ideas.

Abstract

What is the relationship between space and time in the human mind? Studies in adults show an asymmetric relationship between mental representations of these basic dimensions of experience: representations of time depend on space more than representations of space depend on time. Here we investigated the relationship between space and time in the developing mind. Native Greek-speaking children (N=99) watched movies of two animals traveling along parallel paths for different distances or durations and judged the spatial and temporal aspects of these events (e.g., Which animal went for a longer time, or a longer distance?) Results showed a reliable cross-dimensional asymmetry: for the same stimuli, spatial information influenced temporal judgments more than temporal information influenced spatial judgments. This pattern was robust to variations in the age of the participants and the type of language used to elicit responses. This finding demonstrates a continuity between space-time representations in children and adults, and informs theories of analog magnitude representation.

Abstract

In this paper we explore the feasibility of artificial (formal) grammar recognition (AGR) using spiking neural networks. A biologically inspired minicolumn architecture is designed as the basic computational unit. A network topography is defined based on the minicolumn architecture, here referred to as nodes, connected with excitatory and inhibitory connections. Nodes in the network represent unique internal states of the grammar’s finite state machine (FSM). Future work to improve the performance of the networks is discussed. The modeling framework developed can be used by neurophysiological research to implement network layouts and compare simulated performance characteristics to actual subject performance.

Abstract

When we speak, we spontaneously produce gestures (co-speech gestures). Co-speech gestures and speech production are closely interlinked. However, the exact nature of the link is still under debate. To addressed the question that whether co-speech gestures originate from the speech production system or from a system independent of the speech production, the present study examined the relationship between co-speech and co-thought gestures. Co-thought gestures, produced during silent thinking without speaking, presumably originate from a system independent of the speech production processes. We found a positive correlation between the production frequency of co-thought and co-speech gestures, regardless the communicative function that co-speech gestures might serve. Therefore, we suggest that co-speech gestures and co-thought gestures originate from a common system that is independent of the speech production processes

Abstract

Listeners process spoken language in ways which are adapted to the phonological structure of their native language. As a consequence, non-native speakers do not listen to a language in the same way as native speakers; moreover, listeners may use their native language listening procedures inappropriately with foreign input. With sufficient experience, however, it may be possible to inhibit this latter (counter-productive) behavior.

Abstract

English and French listeners performed two tasks - click location and speeded click detection - with both English and French sentences, closely matched for syntactic and phonological structure. Clicks were located more accurately in open- than in closed-class words in both English and French; they were detected more rapidly in open- than in closed-class words in English, but not in French. The two listener groups produced the same pattern of responses, suggesting that higher-level linguistic processing was not involved in these tasks.

Abstract

Two phoneme goodness rating experiments addressed the role of orthographic knowledge in the evaluation of speech sounds. Ratings for the best tokens of /s/ were higher in words spelled with S (e.g., bless) than in words where /s/ was spelled with C (e.g., voice). This difference did not appear for analogous nonwords for which every lexical neighbour had either S or C spelling (pless, floice). Models of phonemic processing incorporating obligatory influence of lexical information in phonemic processing cannot explain this dissociation; the data are consistent with models in which phonemic decisions are not subject to necessary top-down lexical influence.

Abstract

The performance of automatic speech recognition systems is usually assessed in terms of error rate. Human speech recognition produces few errors, but relative difficulty of processing can be assessed via response time techniques. We report the construction of a measure analogous to response time in a machine recognition system. This measure may be compared directly with human response times. We conducted a trial comparison of this type at the phoneme level, including both tense and lax vowels and a variety of consonant classes. The results suggested similarities between human and machine processing in the case of consonants, but differences in the case of vowels.

Abstract

Word blends combine fragments from two words, either in speech errors or when a new word is created. Previous work has demonstrated that in Japanese, such blends preserve moraic structure; in English they do not. A similar effect of moraic structure is observed in perceptual research on segmentation of continuous speech in Japanese; English listeners, by contrast, exploit stress units in segmentation, suggesting that a general rhythmic constraint may underlie both findings. The present study examined whether mis parallel would also hold for word blends. In spontaneous English polysyllabic blends, the source words were significantly more likely to be split before a strong than before a weak (unstressed) syllable, i.e. to be split at a stress unit boundary. In an experiment in which listeners were asked to identify the source words of blends, significantly more correct detections resulted when splits had been made before strong syllables. Word blending, like speech segmentation, appears to be constrained by language rhythm.

Abstract

In a 50,000-word corpus of spoken British English the occurrence of words embedded within other words is reported. Within-word embedding in this real speech sample is common, and analogous to the extent of embedding observed in the vocabulary. Imposition of a syllable boundary matching constraint reduces but by no means eliminates spurious embedding. Embedded words are most likely to overlap with the beginning of matrix words, and thus may pose serious problems for speech recognisers.

Abstract

In a production experiment on Bulgarian that was based on a previous study on Dutch [1], we investigated the role of prosody when linguistic and extra-linguistic information coincide or contradict. Speakers described abnormally colored fruits in conditions where contrastive focus and discourse relations were varied. We found that the coincidence of contrast and abnormality enhances accentuation in Bulgarian as it did in Dutch. Surprisingly, when both factors are in conflict, the prosodic prominence of abnormality often overruled focus accentuation in both Bulgarian and Dutch, though the languages also show marked differences.

Abstract

Humans readily generalize, applying prior knowledge to novel situations and stimuli. Advances in machine learning have begun to approximate and even surpass human performance, but these systems struggle to generalize what they have learned to untrained situations. We present a model based on wellestablished neurocomputational principles that demonstrates human-level generalisation. This model is trained to play one video game (Breakout) and performs one-shot generalisation to a new game (Pong) with different characteristics. The model
generalizes because it learns structured representations that are functionally symbolic (viz., a role-filler binding calculus) from unstructured training data. It does so without feedback, and without requiring that structured representations are specified a priori. Specifically, the model uses neural co-activation to discover which characteristics of the input are invariant and to learn relational predicates, and oscillatory regularities in network firing to bind predicates to arguments. To our knowledge,
this is the first demonstration of human-like generalisation in a machine system that does not assume structured representa-
tions to begin with.

Abstract

This paper investigates how listeners process regular pronunciation variants, resulting from simple general reduction processes. Study 1 shows that when listeners are presented with new words, they store the pronunciation variants presented to them, whether these are unreduced or reduced. Listeners thus store information on word-specific pronunciation variation. Study 2 suggests that if participants are presented with regularly reduced pronunciations, they also reconstruct and store the corresponding unreduced pronunciations. These unreduced pronunciations apparently have special status. Together the results support hybrid models of speech processing, assuming roles for both exemplars and abstract representations.

Abstract

We present a neural-symbolic learning model of sentence production which displays strong semantic systematicity and recursive productivity. Using this model, we provide evidence for the data-driven learnability of complex yes/no- questions.

Abstract

Los idiomas sudamericanas tienen una diversidad de sistemas numéricos, desde sistemas con solamente dos o tres términos en algunos idiomas amazónicos hasta sistemas con numerales extendiendo a miles. Una mirada al sistema del idioma cha’palaa de Ecuador demuestra rasgos de base-2, base-5, base-10 y base-20, ligados a diferentes etapas de cambio, desarrollo y contacto lingüístico. Conocer estas etapas nos permite proponer algunas correlaciones con lo que conocemos de la historia de contactos culturales en la región. The South American languages have diverse types of numeral systems, from systems of just two or three terms in some Amazonian languages to systems extending into the thousands. A look a the system of the Cha'palaa language of Ecuador demonstrates base-2, base-5, base-10 and base-20 features, linked to different stages of change, development and language contact. Learning about these stages permits up to propose some correlations between them and what we know about the history of cultural contact in the region.

Abstract

In a 2x2 event-related FMRI study we find support for the idea that the inferior frontal cortex, centered on Broca’s region and its homologue, is involved in constructive unification operations during the structure-building process in parsing for comprehension. Tentatively, we provide evidence for a role of the dorsolateral prefrontal cortex centered on BA 9/46 in the control component of the language system. Finally, the left temporo-parietal cortex, in the vicinity of Wernicke’s region, supports the interaction between the syntax of gender agreement and sentence-level semantics.

Abstract

Human implicit learning can be investigated with implicit artificial grammar learning, a simple model for aspects of natural language acquisition. In this paper we investigate the remaining effect of modality transfer in syntactic classification of an acquired grammatical sequence structure after implicit grammar acquisition. Participants practiced either on acoustically presented syllable sequences or visually presented consonant letter sequences. During classification we independently manipulated the statistical frequency-based and rule-based characteristics of the classification stimuli. Participants performed reliably above chance on the within modality classification task although more so for those working on syllable sequence acquisition. These subjects were also the only group that kept a significant performance level in transfer classification. We speculate that this finding is of particular relevance in consideration of an ecological validity in the input signal in the use of artificial grammar learning and in language learning paradigms at large.

Abstract

This paper presents a new architecture for optical burst switched networks where the control plane of the network functions in a cooperative manner. Each node interprets the data conveyed by the control packet and forwards it to the next nodes, making the control plane of the network distribute the relevant information to all the nodes in the network. A cooperation transmission tree is used, thus allowing all the nodes to store the information related to the traffic management in the network, and enabling better network resource planning at each node. A model of this network architecture is proposed, and its performance is evaluated.

Abstract

In this paper we introduce Functional Data Analysis (FDA) as a tool for analyzing dynamic transitions in speech signals. FDA makes it possible to perform statistical analyses of sets of mathematical functions in the same way as classical multivariate analysis treats scalar measurement data. We illustrate the use of FDA with a reduction phenomenon affecting the French word c'était /setε/ 'it was', which can be reduced to [stε] in conversational speech. FDA reveals that the dynamics of the transition from [s] to [t] in fully reduced cases may still be different from the dynamics of [s] - [t] transitions in underlying /st/ clusters such as in the word stage.

Abstract

Present-day sentence generators are often in-capable of producing a wide variety of well-formed elliptical versions of coordinated clauses, in particular, of combined elliptical phenomena (Gapping, Forward and Back-ward Conjunction Reduction, etc.). The ap-plicability of the various types of clausal co-ordinate ellipsis (CCE) presupposes detailed comparisons of the syntactic properties of the coordinated clauses. These nonlocal comparisons argue against approaches based on local rules that treat CCE structures as special cases of clausal coordination. We advocate an alternative approach where CCE rules take the form of postediting rules ap-plicable to nonelliptical structures. The ad-vantage is not only a higher level of modu-larity but also applicability to languages be-longing to different language families. We describe a language-neutral module (called Elleipo; implemented in JAVA) that gener-ates as output all major CCE versions of co-ordinated clauses. Elleipo takes as input linearly ordered nonelliptical coordinated clauses annotated with lexical identity and coreferentiality relationships between words and word groups in the conjuncts. We dem-onstrate the feasibility of a single set of postediting rules that attains multilingual coverage.

Abstract

There is evidence that the process of executing a planned utterance involves the use of both preceding-context and top-down cues. Utterance-initial words are cued only by the top-down plan. In contrast, non-initial words are cued both by top-down cues and preceding-context cues. Co-existence of both cue types raises the question of how they interact during learning. We argue that this interaction is competitive: items that tend to be preceded by predictive preceding-context cues are harder to activate from the plan without this predictive context. A novel computational model of this competition is developed. The model is tested on a corpus of repetition disfluencies and shown to account for the influences on patterns of restarts during production. In particular, this model predicts a novel Initiation Effect: following an interruption, speakers re-initiate production from words that tend to occur in utterance-initial position, even when they are not initial in the interrupted utterance.

Abstract

The representations generated by many mod-
els of language (word embeddings, recurrent
neural networks and transformers) correlate
to brain activity recorded while people read.
However, these decoding results are usually
based on the brain’s reaction to syntactically
and semantically sound language stimuli. In
this study, we asked: how does an LSTM (long
short term memory) language model, trained
(by and large) on semantically and syntac-
tically intact language, represent a language
sample with degraded semantic or syntactic
information? Does the LSTM representation
still resemble the brain’s reaction? We found
that, even for some kinds of nonsensical lan-
guage, there is a statistically significant rela-
tionship between the brain’s activity and the
representations of an LSTM. This indicates
that, at least in some instances, LSTMs and the
human brain handle nonsensical data similarly.

Abstract

Listening to a speaker while hearing another speaker talks is a challenging task for elderly listeners. We show that elderly listeners over the age of 65 with various degrees of age-related hearing loss benefit in this situation from also seeing the speaker they intend to listen to. In a phoneme monitoring task, listeners monitored the speech of a target speaker for either the phoneme /p/ or /k/ while simultaneously hearing a competing speaker. Critically, on some trials, the target speaker was also visible. Elderly listeners benefited in their response times and accuracy levels from seeing the target speaker when monitoring for the less visible /k/, but more so when monitoring for the highly visible /p/. Visual speech therefore aids elderly listeners not only by providing segmental information about the target phoneme, but also by providing more global information that allows for better performance in this adverse listening situation.

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

A central question in the psycholinguistic study of multilingualism is how syntax is shared across languages. We implement a model to investigate whether error-based implicit learning can provide an account of cross-language structural priming. The model is based on the Dual-path model of
sentence-production (Chang, 2002). We implement our model using the Bilingual version of Dual-path (Tsoukala, Frank, & Broersma, 2017). We answer two main questions: (1) Can structural priming of active and passive constructions occur between English and Spanish in a bilingual version of the Dual-
path model? (2) Does cross-language priming differ quantitatively from within-language priming in this model? Our results show that cross-language priming does occur in the model. This finding adds to the viability of implicit learning as an account of structural priming in general and cross-language
structural priming specifically. Furthermore, we find that the within-language priming effect is somewhat stronger than the cross-language effect. In the context of mixed results from
behavioral studies, we interpret the latter finding as an indication that the difference between cross-language and within-
language priming is small and difficult to detect statistically.

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

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

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

Languages differ in the degree of semantic flexibility of their syntactic roles. For example, Eng-
lish and Indonesian are considered more flexible with regard to the semantics of subjects,
whereas German and Japanese are less flexible. In Hawkins’ classification, more flexible lan-
guages are said to have a loose fit, and less flexible ones are those that have a tight fit. This
classification has been based on manual inspection of example sentences. The present paper
proposes a new, quantitative approach to deriving the measures of looseness and tightness from
corpora. We use corpora of online news from the Leipzig Corpora Collection in thirty typolog-
ically and genealogically diverse languages and parse them syntactically with the help of the
Universal Dependencies annotation software. Next, we compute Mutual Information scores for
each language using the matrices of lexical lemmas and four syntactic dependencies (intransi-
tive subjects, transitive subject, objects and obliques). The new approach allows us not only to
reproduce the results of previous investigations, but also to extend the typology to new lan-
guages. We also demonstrate that verb-final languages tend to have a tighter relationship be-
tween lexemes and syntactic roles, which helps language users to recognize thematic roles early
during comprehension.

Abstract

Children learn language from the speech in their home environment. Recent work shows that more infant-directed speech
(IDS) leads to stronger lexical development. But what makes IDS a particularly useful learning signal? Here, we expand on an attention-based account first proposed by Räsänen et al. (2018): that prosodic modifications make IDS less predictable, and thus more interesting. First, we reproduce the critical finding from Räsänen et al.: that lab-recorded IDS pitch is less predictable compared to adult-directed speech (ADS). Next, we show that this result generalizes to the home language environment, finding that IDS in daylong recordings is also less predictable than ADS but that this pattern is much less robust than for IDS recorded in the lab. These results link experimental work on attention and prosodic modifications of IDS to real-world language-learning environments, highlighting some challenges of scaling up analyses of IDS to larger datasets that better capture children’s actual input.

Abstract

Lexical stress is realised similarly in English, German, and
Dutch. On a suprasegmental level, stressed syllables tend to be
longer and more acoustically salient than unstressed syllables;
segmentally, vowels in unstressed syllables are often reduced.
The frequency of unreduced unstressed syllables (where only
the suprasegmental cues indicate lack of stress) however,
differs across the languages. The present studies test whether
listener behaviour is affected by these vocabulary differences,
by investigating German listeners’ use of suprasegmental cues
to lexical stress in German and English word recognition. In a
forced-choice identification task, German listeners correctly
assigned single-syllable fragments (e.g., Kon-) to one of two
words differing in stress (KONto, konZEPT). Thus, German
listeners can exploit suprasegmental information for
identifying words. German listeners also performed above
chance in a similar task in English (with, e.g., DIver, diVERT),
i.e., their sensitivity to these cues also transferred to a nonnative
language. An English listener group, in contrast, failed
in the English fragment task. These findings mirror vocabulary
patterns: German has more words with unreduced unstressed
syllables than English does.

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

This paper presents lexical statistics on the pattern of occurrence of words embedded in other words. We report the results of an analysis of 25000 words, varying in length from two to six syllables, extracted from a phonetically-coded English dictionary (The Longman Dictionary of Contemporary English). Each syllable, and each string of syllables within each word was checked against the dictionary. Two analyses are presented: the first used a complete list of polysyllables, with look-up on the entire dictionary; the second used a sublist of content words, counting only embedded words which were themselves content words. The results have important implications for models of human speech recognition. The efficiency of these models depends, in different ways, on the number and location of words within words.

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

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

We report two experiments in which vowels and steady-state consonants served as targets in a speeded detection task. In the first experiment, two vowels were compared with one voiced and once unvoiced fricative. Response times (RTs) to the vowels were longer than to the fricatives. The error rate was higher for the consonants. Consonants in word-final position produced the shortest RTs, For the vowels, RT correlated negatively with target duration. In the second experiment, the same two vowel targets were compared with two nasals. This time there was no significant difference in RTs, but the error rate was still significantly higher for the consonants. Error rate and length correlated negatively for the vowels only. We conclude that RT differences between phonemes are independent of vocalic or consonantal status. Instead, we argue that the process of phoneme detection reflects more finely grained differences in acoustic/articulatory structure within the phonemic repertoire.

Abstract

Automating robot behavior in a specific situation is an active area of research. There are several approaches available in the literature of robotics to cater for the automatic behavior of a robot. However, when it comes to humanoids or human-robot interaction in general, the area has been less explored. In this paper, a pseudo-randomization approach has been introduced to automatize the gestures and facial expressions of an interactive humanoid robot called ROBIN based on its mental state. A significant number of gestures and facial expressions have been implemented to allow the robot more options to perform a relevant action or reaction based on visual stimuli. There is a display of noticeable differences in the behaviour of the robot for the same stimuli perceived from an interaction partner. This slight autonomous behavioural change in the robot clearly shows a notion of automation in behaviour. The results from experimental scenarios and human-centered evaluation of the system help validate the approach.

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

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

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

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

Due to its numerous bibliometric entries of scholarly articles and connected information Wikidata can serve as an open and rich
source for deep scientometrical analyses. However, there are currently certain limitations: While 31.5% of all Wikidata entries represent scientific articles, only 8.9% are entries describing a person and the number
of entries researcher is accordingly even lower. Another issue is the frequent absence of established relations between the scholarly article item and the author item although the author is already listed in Wikidata.
To fill this gap and to improve the content of Wikidata in general, we established a workflow for matching authors and scholarly publications by integrating data from the ORCID (Open Researcher and Contributor ID) database. By this approach we were able to extend Wikidata by more than 12k author-publication relations and the method can be
transferred to other enrichments based on ORCID data. This is extension is beneficial for Wikidata users performing bibliometrical analyses or using such metadata for other purposes.

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

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

In face-to-face conversation, recipients might use the bodily movements of the speaker (e.g. gestures) to facilitate language processing. It has been suggested that one way through which this facilitation may happen is prediction. However, for this to be possible, gestures would need to precede speech, and it is unclear whether this is true during natural conversation.
In a corpus of Dutch conversations, we annotated hand gestures that represent semantic information and occurred during questions, and the word(s) which corresponded most closely to the gesturally depicted meaning. Thus, we tested whether representational gestures temporally precede their lexical affiliates. Further, to see whether preceding gestures may indeed facilitate language processing, we asked whether the gesture-speech asynchrony predicts the response time to the question the gesture is part of.
Gestures and their strokes (most meaningful movement component) indeed preceded the corresponding lexical information, thus demonstrating their predictive potential. However, while questions with gestures got faster responses than questions without, there was no evidence that questions with larger gesture-speech asynchronies get faster responses. These results suggest that gestures indeed have the potential to facilitate predictive language processing, but further analyses on larger datasets are needed to test for links between asynchrony and processing advantages.