Publications

Abstract

When we communicate with others, we often repeat aspects of each other's communicative behavior such as sentence structures and words. Such behavioral alignment has been mostly studied for speech or text. Yet, language use is mostly multimodal, flexibly using speech and gestures to convey messages. Here, we explore the use of alignment in speech (words) and co-speech gestures (iconic gestures) in a referential communication task aimed at finding labels for novel objects in interaction. In particular, we investigate how people flexibly use lexical and gestural alignment to create shared labels for novel objects and whether alignment in speech and gesture are related over time. The present study shows that interlocutors establish shared labels multimodally, and alignment in words and iconic gestures are used throughout the interaction. We also show that the amount of lexical alignment positively associates with the amount of gestural alignment over time, suggesting a close relationship between alignment in the vocal and manual modalities.

Abstract

Most work on part-of-speech (POS) tagging is focused on high resource languages, or examines low-resource and active learning settings through simulated studies. We evaluate POS tagging techniques on an actual endangered language, Griko. We present a resource that contains 114 narratives in Griko, along with sentence-level translations in Italian, and provides gold annotations for the test set. Based on a previously collected small corpus, we investigate several traditional methods, as well as methods that take advantage of monolingual data or project cross-lingual POS tags. We show that the combination of a semi-supervised method with cross-lingual transfer is more appropriate for this extremely challenging setting, with the best tagger achieving an accuracy of 72.9%. With an applied active learning scheme, which we use to collect sentence-level annotations over the test set, we achieve improvements of more than 21 percentage points

Abstract

Recognizing the limitations of standard vision assessments in capturing the real-world capabilities of individuals with low vision, we investigated the potential of the Seguin Form Board Test (SFBT), a widely-used intelligence assessment employing a visuo-haptic shape-fitting task, as an estimator of vision's practical utility. We present findings from 23 children from India, who underwent treatment for congenital bilateral dense cataracts, and 21 control participants. To assess the development of functional visual ability, we conducted the SFBT and the standard measure of visual acuity, before and longitudinally after treatment. We observed a dissociation in the development of shape-fitting and visual acuity. Improvements of patients' shape-fitting preceded enhancements in their visual acuity after surgery and emerged even with acuity worse than that of control participants. Our findings highlight the importance of incorporating multi-modal and cognitive aspects into evaluations of visual proficiency in low-vision conditions, to better reflect vision's impact on daily activities.

Abstract

The current study investigates whether mentalizing, or taking the perspective of your interlocutor, plays an essential role throughout a conversation or whether it is mostly used in reaction to misunderstandings. This study is the first to use a brain-imaging method, MEG, to answer this question. In a first phase of the experiment, MEG participants interacted "live" with a confederate who set naming precedents for certain pictures. In a later phase, these precedents were sometimes broken by a speaker who named the same picture in a different way. This could be done by the same speaker, who set the precedent, or by a different speaker. Source analysis of MEG data showed that in the 800 ms before the naming, when the picture was already on the screen, episodic memory and language areas were activated, but no mentalizing areas, suggesting that the speaker's naming intentions were not anticipated by the listener on the basis of shared experiences. Mentalizing areas only became activated after the same speaker had broken a precedent, which we interpret as a reaction to the violation of conversational pragmatics.

Abstract

Rapid Automatized Naming (RAN) is a powerful tool for pre- dicting future reading skill. A person’s ability to quickly name symbols as they scan a table is related to higher-level reading proficiency in adults and is predictive of future literacy gains in children. However, noticeable differences are present in the strategies or patterns within groups having similar task comple- tion times. Thus, a further stratification of RAN dynamics may lead to better characterization and later intervention to support reading skill acquisition. In this work, we analyze the dynamics of the eyes, voice, and the coordination between the two during performance. It is shown that fast performers are more similar to each other than to slow performers in their patterns, but not vice versa. Further insights are provided about the patterns of more proficient subjects. For instance, fast performers tended to exhibit smoother behavior contours, suggesting a more sta- ble perception-production process.

Abstract

Learning a language involves learning how to map specific forms onto their associated meanings. Such mappings can utilise arbitrariness and non-arbitrariness, yet, our understanding of how these two systems operate at different stages of vocabulary development is still not fully understood. The Sound-Symbolism Bootstrapping Hypothesis (SSBH) proposes that sound-symbolism is essential for word learning to commence, but empirical evidence of exactly how sound-symbolism influences language learning is still sparse. It may be the case that sound-symbolism supports acquisition of categories of meaning, or that it enables acquisition of individualized word meanings. In two Experiments where participants learned form-meaning mappings from either sound-symbolic or arbitrary languages, we demonstrate the changing roles of sound-symbolism and arbitrariness for different vocabulary sizes, showing that sound-symbolism provides an advantage for learning of broad categories, which may then transfer to support learning individual words, whereas an arbitrary language impedes acquisition of categories of sound to meaning.

Abstract

We investigate children’s online predictive processing as it occurs naturally, in conversation. We showed 1–7 year-olds short videos of improvised conversation between puppets, controlling for available linguistic information through phonetic manipulation. Even one- and two-year-old children made accurate and spontaneous predictions about when a turn-switch would occur: they gazed at the upcoming speaker before they heard a response begin. This predictive skill relies on both lexical and prosodic information together, and is not tied to either type of information alone. We suggest that children integrate prosodic, lexical, and visual information to effectively predict upcoming linguistic material in conversation.

Abstract

Two groups of listeners, with Dutch and British English as their native language judged stimuli in Dutch and British English, respectively, on the scales CONFIDENT vs. NOT CONFIDENT and FRIENDLY vs. NOT FRIENDLY, two meanings derived from Ohala's universal Frequency Code. The stimuli, which were lexically equivalent, were varied in pitch contour and pitch range. In both languages, the perceived degree of confidence decreases and that of friendliness increases when the pitch range is raised, as predicted by the Frequency Code. However, at identical pitch ranges, British English is perceived as more confident and more friendly than Dutch. We argue that this difference in degree of the use of the Frequency Code is due to the difference in the standard pitch ranges of Dutch and British English.

Abstract

In natural communication, we typically both see and hear our conversation partner. Speech comprehension thus requires the integration of auditory and visual information from the speech signal. This is for instance evidenced by the Manual McGurk effect, where the perception of lexical stress is biased towards the syllable that has a beat gesture aligned to it. However, there is considerable individual variation in how heavily gestural timing is weighed as a cue to stress. To assess within-individualconsistency, this study investigated the test-retest reliability of the Manual McGurk effect. We reran an earlier Manual McGurk experiment with the same participants, over 1.5 years later. At the group level, we successfully replicated the Manual McGurk effect with a similar effect size. However, a correlation of the by-participant effect sizes in the two identical experiments indicated that there was only a weak correlation between both tests, suggesting that the weighing of gestural information in the perception of lexical stress is stable at the group level, but less so in individuals. Findings are discussed in comparison to other measures of audiovisual integration in speech perception. Index Terms: Audiovisual integration, beat gestures, lexical stress, test-retest reliability

Abstract

Speaker diarization (answering 'who spoke when') is a widely researched subject within speech technology. Numerous experiments have been run on datasets built from broadcast news, meeting data, and call centers—the task sometimes appears close to being solved. Much less work has begun to tackle the hardest diarization task of all: spontaneous conversations in real-world settings. Such diarization would be particularly useful for studies of language acquisition, where researchers investigate the speech children produce and hear in their daily lives. In this paper, we study audio gathered with a recorder worn by small children as they went about their normal days. As a result, each child was exposed to different acoustic environments with a multitude of background noises and a varying number of adults and peers. The inconsistency of speech and noise within and across samples poses a challenging task for speaker diarization systems, which we tackled via retraining and data augmentation techniques. We further studied sources of structured variation across raw audio files, including the impact of speaker type distribution, proportion of speech from children, and child age on diarization performance. We discuss the extent to which these findings might generalize to other samples of speech in the wild.

Abstract

In two experiments, Mandarin listeners resolved potential syntactic ambiguities in spoken utterances in (a) their native language (L1) and (b) English which they had learned as a second language (L2). A new disambiguation task was used, requiring speeded responses to select the correct meaning for structurally ambiguous sentences. Importantly, the ambiguities used in the study are identical in Mandarin and in English, and production data show that prosodic disambiguation of this type of ambiguity is also realised very similarly in the two languages. The perceptual results here showed however that listeners’ response patterns differed for L1 and L2, although there was a significant increase in similarity between the two response patterns with increasing exposure to the L2. Thus identical ambiguity and comparable disambiguation patterns in L1 and L2 do not lead to immediate application of the appropriate L1 listening strategy to L2; instead, it appears that such a strategy may have to be learned anew for the L2.

Abstract

Previous research has shown that listeners use the prosodic structure of utterances in a predictive fashion in sentence comprehension, to direct attention to accented words. Acoustically identical words spliced into sentence contexts arc responded to differently if the prosodic structure of the context is \ aricd: when the preceding prosody indicates that the word will he accented, responses are faster than when the preceding prosodv is inconsistent with accent occurring on that word. In the present series of experiments speech hybridisation techniques were first used to interchange the timing patterns within pairs of prosodic variants of utterances, independently of the pitch and intensity contours. The time-adjusted utterances could then serve as a basis lor the orthogonal manipulation of the three prosodic dimensions of pilch, intensity and rhythm. The overall pattern of results showed that when listeners use prosody to predict accent location, they do not simply rely on a single prosodic dimension, hut exploit the interaction between pitch, intensity and rhythm.

Abstract

Using a phoneme detection task, the present series of
experiments examines whether listeners can entrain to
different combinations of prosodic cues to predict where focus
will fall in an utterance. The stimuli were recorded by four
female native speakers of Australian English who happened to
have used different prosodic cues to produce sentences with
prosodic focus: a combination of duration cues, mean and
maximum F0, F0 range, and longer pre-target interval before
the focused word onset, only mean F0 cues, only pre-target
interval, and only duration cues. Results revealed that listeners
can entrain in almost every condition except for where
duration was the only reliable cue. Our findings suggest that
listeners are flexible in the cues they use for focus processing.

Abstract

Listeners adapt rapidly to previously unheard talkers by
adjusting phoneme categories using lexical knowledge, in a
process termed lexically-guided perceptual learning. Although
this is firmly established for listening in the native language
(L1), perceptual flexibility in second languages (L2) is as yet
less well understood. We report two experiments examining L1
and L2 perceptual learning, the first in Mandarin-English late
bilinguals, the second in Australian learners of Mandarin. Both
studies showed stronger learning in L1; in L2, however,
learning appeared for the English-L1 group but not for the
Mandarin-L1 group. Phonological mapping differences from
the L1 to the L2 are suggested as the reason for this result.

Abstract

Two lexical decision experiments in Cantonese are described in which the recognition of spoken target words as a function of phonological similarity to a preceding prime is investigated. Phonological similaritv in first syllables produced inhibition, while similarity in second syllables led to facilitation. Differences between syllables in tonal and segmental structure had generally similar effects.

Abstract

Two lexical decision studies examined the effects of single-phoneme mismatches on lexical activation in spoken-word recognition. One study was carried out in English, and involved spoken primes and visually presented lexical decision targets. The other study was carried out in Dutch, and primes and targets were both presented auditorily. Facilitation was found only for spoken targets preceded immediately by spoken primes; no facilitation occurred when targets were presented visually, or when intervening input occurred between prime and target. The effects of vowel mismatches and consonant mismatches were equivalent.

Abstract

Vocabularies contain hundreds of thousands of words built from only a handful of phonemes, so that inevitably longer words tend to contain shorter ones. In many languages (but not all) such embedded words occur more often word-initially than word-finally, and this asymmetry, if present, has farreaching consequences for spoken-word recognition. Prior research had ascribed the asymmetry to suffixing or to effects of stress (in particular, final syllables containing the vowel schwa). Analyses of the standard French vocabulary here reveal an effect of suffixing, as predicted by this account, and further analyses of an artificial variety of French reveal that extensive final schwa has an independent and additive effect in promoting the embedding asymmetry.

Abstract

Spatial congruity effects have often been interpreted as evidence for metaphorical thinking, but an alternative markedness-based account challenges this view. In two experiments, we directly compared metaphor and markedness explanations for spatial congruity effects, using musical pitch as a testbed. English speakers who talk about pitch in terms of spatial height were tested in speeded space-pitch compatibility tasks. To determine whether space-pitch congruency effects could be elicited by any marked spatial continuum, participants were asked to classify high- and low-frequency pitches as 'high' and 'low' or as 'front' and 'back' (both pairs of terms constitute cases of marked continuums). We found congruency effects in high/low conditions but not in front/back conditions, indicating that markedness is not sufficient to account for congruity effects (Experiment 1). A second experiment showed that congruency effects were specific to spatial words that cued a vertical schema (tall/short), and that congruity effects were not an artifact of polysemy (e.g., 'high' referring both to space and pitch). Together, these results suggest that congruency effects reveal metaphorical uses of spatial schemas, not markedness effects.

Abstract

Biologically inspired spiking networks are an important tool to study the nature of computation and cognition in neural systems. In this work, we investigate the representational capacity of spiking networks engaged in an identity mapping task. We compare two schemes for encoding symbolic input, one in which input is injected as a direct current and one where input is delivered as a spatio-temporal spike pattern. We test the ability of networks to discriminate their input as a function of the number of distinct input symbols. We also compare performance using either membrane potentials or filtered spike trains as state variable. Furthermore, we investigate how the circuit behavior depends on the balance between excitation and inhibition, and the degree of synchrony and regularity in its internal dynamics. Finally, we compare different linear methods of decoding population activity onto desired target labels. Overall, our results suggest that even this simple mapping task is strongly influenced by design choices on input encoding, state-variables, circuit characteristics and decoding methods, and these factors can interact in complex ways. This work highlights the importance of constraining computational network models of behavior by available neurobiological evidence.

Abstract

In recent years, large scale initiatives like CLARIN set out to overcome the notorious heterogeneity of metadata formats in the domain of language resource. The CLARIN Component Metadata Infrastructure established means for flexible resouce descriptions for the domain of language resources. The Data Category Registry ISOcat and the accompanying Relation Registry foster semantic interoperability within the growing heterogeneous collection of metadata records. This paper describes the CMD Infrastructure focusing on the facilities for semantic mapping, and gives also an overview of the current status in the joint component metadata domain.

Abstract

We analyze the problem of response suggestion in a closed domain along a real-world scenario of a digital library. We present a text-processing pipeline to generate question-answer pairs from chat transcripts. On this limited amount of training data, we compare retrieval-based, conditioned-generation, and dedicated representation learning approaches for response suggestion. Our results show that retrieval-based methods that strive to find similar, known contexts are preferable over parametric approaches from the conditioned-generation family, when the training data is limited. We, however, identify a specific representation learning approach that is competitive to the retrieval-based approaches despite the training data limitation.

Abstract

We present multi-modal adversarial autoencoders for recommendation and evaluate them on two different tasks: citation recommendation and subject label recommendation. We analyze the effects of adversarial regularization, sparsity, and different input modalities. By conducting 408 experiments, we show that adversarial regularization consistently improves the performance of autoencoders for recommendation. We demonstrate, however, that the two tasks differ in the semantics of item co-occurrence in the sense that item co-occurrence resembles relatedness in case of citations, yet implies diversity in case of subject labels. Our results reveal that supplying the partial item set as input is only helpful, when item co-occurrence resembles relatedness. When facing a new recommendation task it is therefore crucial to consider the semantics of item co-occurrence for the choice of an appropriate model.

Abstract

We propose a Random-Forest based sign language identification system. The system uses low-level visual features and is based on the hypothesis that sign languages have varying distributions of phonemes (hand-shapes, locations and movements). We evaluated the system on two sign languages -- British SL and Greek SL, both taken from a publicly available corpus, called Dicta Sign Corpus. Achieved average F1 scores are about 95% - indicating that sign languages can be identified with high accuracy using only low-level visual features.

Abstract

We present a vision-based method for signer diarization -- the task of automatically determining "who signed when?" in a video. This task has similar motivations and applications as speaker diarization but has received little attention in the literature. In this paper, we motivate the problem and propose a method for solving it. The method is based on the hypothesis that signers make more movements than their interlocutors. Experiments on four videos (a total of 1.4 hours and each consisting of two signers) show the applicability of the method. The best diarization error rate (DER) obtained is 0.16.

Abstract

This paper presents a Native Language Identification (NLI) system based on TF-IDF weighting schemes and using linear classifiers - support vector machines, logistic regressions and perceptrons. The system was one of the participants of the 2013 NLI Shared Task in the closed-training track, achieving 0.814 overall accuracy for a set of 11 native languages. This accuracy was only 2.2 percentage points lower than the winner's performance. Furthermore, with subsequent evaluations using 10-fold cross-validation (as given by the organizers) on the combined training and development data, the best average accuracy obtained is 0.8455 and the features that contributed to this accuracy are the TF-IDF of the combined unigrams and bigrams of words.

Abstract

We present and solve the speaker diarization problem in a novel way. We hypothesize that the gesturer is the speaker and that identifying the gesturer can be taken as identifying the active speaker. We provide evidence in support of the hypothesis from gesture literature and audio-visual synchrony studies. We also present a vision-only diarization algorithm that relies on gestures (i.e. upper body movements). Experiments carried out on 8.9 hours of a publicly available dataset (the AMI meeting data) show that diarization error rates as low as 15% can be achieved.

Abstract

Conversation requires a substantial amount of coordination between dialogue participants, from managing turn taking to negotiating mutual understanding. Part of this coordination effort surfaces as the reuse of linguistic behaviour across speakers, a process often referred to as alignment. While the presence of linguistic alignment is well documented in the literature, several questions remain open, including the extent to which patterns of reuse across speakers have an impact on the emergence of labelling conventions for novel referents. In this study, we put forward a methodology for automatically detecting shared lemmatised constructions---expressions with a common lexical core used by both speakers within a dialogue---and apply it to a referential communication corpus where participants aim to identify novel objects for which no established labels exist. Our analyses uncover the usage patterns of shared constructions in interaction and reveal that features such as their frequency and the amount of different constructions used for a referent are associated with the degree of object labelling convergence the participants exhibit after social interaction. More generally, the present study shows that automatically detected shared constructions offer a useful level of analysis to investigate the dynamics of reference negotiation in dialogue.

Abstract

Gestures are integral components of face-to-face communication. They unfold over time, often following predictable movement phases of preparation, stroke, and re-
traction. Yet, the prevalent approach to automatic gesture detection treats the problem as binary classification, classifying a segment as either containing a gesture or not, thus failing to capture its inherently sequential and contextual nature. To address this, we introduce a novel framework that reframes the task as a multi-phase sequence labeling problem rather than binary classification. Our model processes sequences of skeletal movements over time windows, uses Transformer encoders to learn contextual embeddings, and leverages Conditional Random Fields to perform sequence labeling. We evaluate our proposal on a large dataset of diverse co-speech gestures in task-oriented face-to-face dialogues. The results consistently demonstrate that our method significantly outperforms strong baseline models in detecting gesture strokes. Furthermore, applying Transformer encoders to learn contextual embeddings from movement sequences substantially improves gesture unit detection. These results highlight our framework’s capacity to capture the fine-grained dynamics of co-speech gesture phases, paving the way for more nuanced and accurate gesture detection and analysis.

Abstract

How are space and time related in the brain? This study contrasts two proposals that make different predictions about the interaction between spatial and temporal magnitudes. Whereas ATOM implies that space and time are symmetrically related, Metaphor Theory claims they are asymmetrically related. Here we investigated whether space and time activate the same neural structures in the inferior parietal cortex (IPC) and whether the activation is symmetric or asymmetric across domains. We measured participants’ neural activity while they made temporal and spatial judgments on the same visual stimuli. The behavioral results replicated earlier observations of a space-time asymmetry: Temporal judgments were more strongly influenced by irrelevant spatial information than vice versa. The BOLD fMRI data indicated that space and time activated overlapping clusters in the IPC and that, consistent with Metaphor Theory, this activation was asymmetric: The shared region of IPC was activated more strongly during temporal judgments than during spatial judgments. We consider three possible interpretations of this neural asymmetry, based on 3 possible functions of IPC.

Abstract

The shape of pitch contours has been shown to have an effect on the perceived duration of vowels. For instance, vowels with high level pitch and vowels with falling contours sound longer than vowels with low level pitch. Depending on whether the
comparison is between level pitches or between level and dynamic contours, these findings have been interpreted in two ways. For inter-level comparisons, where the duration results are the reverse of production results, a hypercorrection strategy in production has been proposed [1]. By contrast, for comparisons between level pitches and dynamic contours, the
longer production data for dynamic contours have been held responsible. We report an experiment with Dutch and Chinese listeners which aimed to show that production data and perception data are each other’s opposites for high, low, falling and rising contours. We explain the results, which are consistent with earlier findings, in terms of the compensatory listening strategy of [2], arguing that the perception effects are due to a perceptual compensation of articulatory strategies and
constraints, rather than that differences in production compensate for psycho-acoustic perception effects.

Abstract

In human face-to-face communication, language comprehension is a multi-modal, situated activity. However, little is known about how we combine information from these different modalities, and how perceived communicative intentions, often signaled through visual signals, such as eye
gaze, may influence this processing. We address this question by simulating a triadic communication context in which a
speaker alternated her gaze between two different recipients. Participants thus viewed speech-only or speech+gesture
object-related utterances when being addressed (direct gaze) or unaddressed (averted gaze). Two object images followed
each message and participants’ task was to choose the object that matched the message. Unaddressed recipients responded significantly slower than addressees for speech-only
utterances. However, perceiving the same speech accompanied by gestures sped them up to a level identical to
that of addressees. That is, when speech processing suffers due to not being addressed, gesture processing remains intact and enhances the comprehension of a speaker’s message

Abstract

What makes some words harder to learn than others in a second language? Although some robust factors have been identified based on small scale experimental studies, many relevant factors are difficult to study in such experiments due to the amount of data necessary to test them. Here, we investigate what factors affect the ease of learning of a word in a second language using a large data set of users learning English as a second language through the Duolingo mobile app. In a regression analysis, we test and confirm the well-studied effect of cognate status on word learning accuracy. Furthermore, we find significant effects for both cross-linguistic semantic alignment and English semantic density, two novel predictors derived from large scale distributional models of lexical semantics. Finally, we provide data on several other psycholinguistically plausible word level predictors. We conclude with a discussion of the limits, benefits and future research potential of using big data for investigating second language learning.

Abstract

Models of cognitive processes often include simplifications, idealisations, and fictionalisations, so how should we learn about cognitive processes from such models? Particularly in cognitive science, when many features of the target system are unknown, it is not always clear which simplifications, idealisations, and so on, are appropriate for a research question, and which are highly misleading. Here we use a case-study from studies of language evolution, and ideas from philosophy of science, to illustrate a robustness approach to learning from models. Robust properties are those that arise across a range of models, simulations and experiments, and can be used to identify key causal structures in the models, and the phenomenon, under investigation. For example, in studies of language evolution, the emergence of compositional structure is a robust property across models, simulations and experiments of cultural transmission, but only under pressures for learnability and expressivity. This arguably illustrates the principles underlying real cases of language evolution. We provide an outline of the robustness approach, including its limitations, and suggest that this methodology can be productively used throughout cognitive science. Perhaps of most importance, it suggests that different modelling frameworks should be used as tools to identify the abstract properties of a system, rather than being definitive expressions of theories.

Abstract

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

Abstract

Second language (L2) research often involves analyses of acoustic measures of fluency. The studies investigating fluency, however, have been difficult to compare because the measures of fluency that were used differed widely. One of the differences between studies concerns the lower cut-off point for silent pauses, which has been set anywhere between 100 ms and 1000 ms. The goal of this paper is to find an optimal cut-off point. We calculate acoustic measures of fluency using different pause thresholds and then relate these measures to a measure of L2 proficiency and to ratings on fluency.

Abstract

Indian Sign Language has limited resources for developing machine learning and data-driven approaches for automated language processing. Though text/audio-based language processing techniques have shown colossal research interest and tremendous improvements in the last few years, Sign Languages still need to catch up due to the need for more resources. To bridge this gap, in this work, we propose iSign: a benchmark for Indian Sign Language (ISL) Processing. We make three primary contributions to this work. First, we release one of the largest ISL-English datasets with more than video-sentence/phrase pairs. To the best of our knowledge, it is the largest sign language dataset available for ISL. Second, we propose multiple NLP-specific tasks (including SignVideo2Text, SignPose2Text, Text2Pose, Word Prediction, and Sign Semantics) and benchmark them with the baseline models for easier access to the research community. Third, we provide detailed insights into the proposed benchmarks with a few linguistic insights into the working of ISL. We streamline the evaluation of Sign Language processing, addressing the gaps in the NLP research community for Sign Languages. We release the dataset, tasks and models via the following website: https://exploration-lab.github.io/iSign/

Abstract

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

Abstract

Why do languages have the categories they do? It has been argued that spatial terms in the world’s languages reflect categories that support highly informative communication, and that this accounts for the spatial categories found across languages. However, this proposal has been tested against only nine languages, and in a limited fashion. Here, we consider two new languages: Maijɨki, an under-documented language of Peruvian Amazonia, and English. We analyze spatial data from these two new languages and the original nine, using thorough and theoretically targeted computational tests. The results support the hypothesis that spatial terms across dissimilar languages enable near-optimally informative communication, over an influential competing hypothesis

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

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

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

Abstract

The past year has seen a steep rise in generative AI systems that claim to be open. But how open are they really? The question of what counts as open source in generative AI is poised to take on particular importance in light of the upcoming EU AI Act that regulates open source systems differently, creating an urgent need for practical openness assessment. Here we use an evidence-based framework that distinguishes 14 dimensions of openness, from training datasets to scientific and technical documentation and from licensing to access methods. Surveying over 45 generative AI systems (both text and text-to-image), we find that while the term open source is widely used, many models are `open weight' at best and many providers seek to evade scientific, legal and regulatory scrutiny by withholding information on training and fine-tuning data. We argue that openness in generative AI is necessarily composite (consisting of multiple elements) and gradient (coming in degrees), and point out the risk of relying on single features like access or licensing to declare models open or not. Evidence-based openness assessment can help foster a generative AI landscape in which models can be effectively regulated, model providers can be held accountable, scientists can scrutinise generative AI, and end users can make informed decisions.

Abstract

Speakers and listeners show an informativity bias in the use and interpretation of color modifiers. For example, speakers use color more often when referring to objects that vary in color than to objects with a prototypical color. Likewise, listeners look away from objects with prototypical colors upon hearing that color mentioned. Here we test whether speakers and listeners account for another factor related to informativity: the strength of the association between lexical categories and color. Our results demonstrate that speakers and listeners' choices are indeed influenced by this factor; as such, it should be integrated into current pragmatic theories of informativity and computational models of color reference.

Abstract

We present the Narrative Brain Dataset, an fMRI dataset that was collected during spoken presentation of short excerpts of three
stories in Dutch. Together with the brain imaging data, the dataset contains the written versions of the stimulation texts. The texts are
accompanied with stochastic (perplexity and entropy) and semantic computational linguistic measures. The richness and unconstrained
nature of the data allows the study of language processing in the brain in a more naturalistic setting than is common for fMRI studies.
We hope that by making NBD available we serve the double purpose of providing useful neural data to researchers interested in natural
language processing in the brain and to further stimulate data sharing in the field of neuroscience of language.

Abstract

For (semi-)automated subject indexing systems in digital libraries, it is often more practical to use metadata such as the title of a publication instead of the full-text or the abstract. Therefore, it is desirable to have good text mining and text classification algorithms that operate well already on the title of a publication. So far, the classification performance on titles is not competitive with the performance on the full-texts if the same number of training samples is used for training. However, it is much easier to obtain title data in large quantities and to use it for training than full-text data. In this paper, we investigate the question how models obtained from training on increasing amounts of title training data compare to models from training on a constant number of full-texts. We evaluate this question on a large-scale dataset from the medical domain (PubMed) and from economics (EconBiz). In these datasets, the titles and annotations of millions of publications are available, and they outnumber the available full-texts by a factor of 20 and 15, respectively. To exploit these large amounts of data to their full potential, we develop three strong deep learning classifiers and evaluate their performance on the two datasets. The results are promising. On the EconBiz dataset, all three classifiers outperform their full-text counterparts by a large margin. The best title-based classifier outperforms the best full-text method by 9.4%. On the PubMed dataset, the best title-based method almost reaches the performance of the best full-text classifier, with a difference of only 2.9%.

Abstract

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

Abstract

Human conversations are inherently multimodal, including auditory speech, visual articulatory cues, and hand gestures. Recent studies demonstrated that the timing of a simple up-and-down hand movement, known as a beat gesture, can affect speech perception. A beat gesture falling on the first syllable of a disyllabic word induces a bias to perceive a strong-weak stress pattern (i.e., “CONtent”), while a beat gesture falling on the second syllable combined with the same acoustics biases towards a weak-strong stress pattern (“conTENT”). This effect, termed the “manual McGurk effect”, has been studied in both in-lab and online studies, employing standard experimental sessions lasting approximately forty minutes. The present work tests whether the manual McGurk effect can be observed in an online short version (“mini-test”) of the original paradigm, lasting only ten minutes. Additionally, we employ two different response modalities, namely a two-alternative forced choice and a visual analog scale. A significant manual McGurk effect was observed with both response modalities. Overall, the present study demonstrates the feasibility of employing a ten-minute manual McGurk mini-test to obtain a measure of gesture-speech integration. As such, it may lend itself for inclusion in large-scale test batteries that aim to quantify individual variation in language processing.

Abstract

The results of a study on perceptual learning are reported. Dutch subjects made lexical decisions on a list of words and nonwords. Embedded in the list were either [f]- or [s]-final words in which the final fricative had been replaced by an ambiguous sound, midway between [f] and [s]. One group of listeners heard ambiguous [f]- final Dutch words like [kara?] (based on karaf, carafe) and unambiguous [s]-final words (e.g., karkas, carcase). A second group heard the reverse (e.g., ambiguous [karka?] and unambiguous karaf). After this training phase, listeners labelled ambiguous fricatives on an [f]- [s] continuum. The subjects who had heard [?] in [f]- final words categorised these fricatives as [f] reliably more often than those who had heard [?] in [s]-final words. These results suggest that speech recognition is dynamic: the system adjusts to the constraints of each particular listening situation. The lexicon can provide this adjustment process with a training signal.

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

With the recent advancements in the field of robotics and the increased focus on having general-purpose robots widely available to the general public, it has become increasingly necessary to pursue research into Human-robot interaction (HRI). While there have been a lot of works discussing frameworks for teaching HRI in educational institutions with a few institutions already offering courses to students, a consensus on the course content still eludes the field. In this work, we highlight a few challenges and opportunities while designing an HRI course from an Indian perspective. These topics warrant further deliberations as they have a direct impact on the design of HRI courses and wider implications for the entire field.

Abstract

The central issues in the study of speech recognition by human listeners (HSR) and of automatic speech recognition (ASR) are clearly comparable; nevertheless the research communities that concern themselves with ASR and HSR are largely distinct. This paper compares the research objectives of the two fields, and attempts to draw informative lessons from one to the other.

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

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

Abstract

Japanese, British English and American English listeners were presented with spoken words in their native language, and asked to mark on a written transcript of each word the first natural division point in the word. The results showed clear and strong patterns of consensus, indicating that listeners have available to them conscious representations of within-word structure. Orthography did not play a strongly deciding role in the results. The patterns of response were at variance with results from on-line studies of speech segmentation, suggesting that the present task taps not those representations used in on-line listening, but levels of representation which may involve much richer knowledge of word-internal structure.

Abstract

Current models of spoken-word recognition assume automatic activation of multiple candidate words fully or partially compatible with the speech input. We propose that listeners make use of this concurrent activation in word play such as punning. Distortion in punning should ideally involve no more than a minimal contrastive deviation between two words, namely a phoneme. Moreover, we propose that this metric of similarity does not presuppose phonemic awareness on the part of the punster. We support these claims with an analysis of modern and traditional puns in Japanese (in which phonemic awareness in language users is not encouraged by alphabetic orthography). For both data sets, the results support the predictions. Punning draws on basic processes of spokenword recognition, common across languages.