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

The present paper assesses discourse-pragmatic factors as a potential explanation for the subject-object assymetry in early child language. It identifies a set of factors which characterize typical situations of informativeness (Greenfield & Smith, 1976), and uses these factors to identify informative arguments in data from four children aged 2;0 through 3;6 learning Inuktitut as a first language. In addition, it assesses the extent of the links between features of informativeness on one hand and lexical vs. null and subject vs. object arguments on the other. Results suggest that a pragmatics account of the subject-object asymmetry can be upheld to a greater extent than previous research indicates, and that several of the factors characterizing informativeness are good indicators of those arguments which tend to be omitted in early child language.

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

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

In lexical stress languages, phonemically identical syllables can differ suprasegmentally (in duration, amplitude, F0). Such stress
cues allow listeners to speed spoken-word recognition by rejecting mismatching competitors (e.g., unstressed set- in settee
rules out stressed set- in setting, setter, settle). Such processing effects have indeed been observed in Spanish, Dutch and German, but English listeners are known to largely ignore stress cues. Dutch and German listeners even outdo English listeners in distinguishing stressed versus unstressed English syllables. This has been attributed to the relative frequency across the stress languages of unstressed syllables with full vowels; in English most unstressed syllables contain schwa, instead, and stress cues on full vowels are thus least often informative in this language. If only informativeness matters, would English listeners who encounter situations where such cues would pay off for them (e.g., learning one of those other stress languages) then shift to using stress cues? Likewise, would stress cue users with English as L2, if mainly using English, shift away from
using the cues in English? Here we report tests of these two questions, with each receiving a yes answer. We propose that
English listeners’ disregard of stress cues is purely pragmatic.

Abstract

Producing lexical stress leads to visible changes on the face, such as longer duration and greater size of the opening of the mouth. Research suggests that these visual cues alone can inform participants about which syllable carries stress (i.e., lip-reading silent videos). This study aims to determine the influence of visual articulatory cues on lexical stress perception in more naturalistic audiovisual settings. Participants were presented with seven disyllabic, Dutch minimal stress pairs (e.g., VOORnaam [first name] & voorNAAM [respectable]) in audio-only (phonetic lexical stress continua without video), video-only (lip-reading silent videos), and audiovisual trials (e.g., phonetic lexical stress continua with video of talker saying VOORnaam or voorNAAM). Categorization data from video-only trials revealed that participants could distinguish the minimal pairs above chance from seeing the silent videos alone. However, responses in the audiovisual condition did not differ from the audio-only condition. We thus conclude that visual lexical stress information on the face, while clearly perceivable, does not play a major role in audiovisual speech perception. This study demonstrates that clear unimodal effects do not always generalize to more naturalistic multimodal communication, advocating that speech prosody is best considered in multimodal settings.

Abstract

The current study presents an agent-based model which simulates the innovation and
competition among lexical items in cases of language contact. It is inspired by relatively
recent historical cases in which the linguistic ecology and sociohistorical context are highly complex. Pidgin and creole genesis offers an opportunity to obtain linguistic facts, social dynamics, and historical demography in a highly segregated society. This provides a solid ground for researching the interaction of populations with different pre-existing language systems, and how different factors contribute to the genesis of the lexicon of a newly generated mixed language. We take into consideration the population dynamics and structures, as well as a distribution of word frequencies related to language use, in order to study how social factors may affect the developmental trajectory of languages. Focusing on the case of Sranan in Suriname, our study shows that it is possible to account for the
composition of its core lexicon in relation to different social groups, contact patterns, and
large population movements.

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

Assimilation of place of articulation across a nasal and a following stop consonant is obligatory in Japanese, but not in Dutch. In four experiments the processing of assimilated forms by speakers of Japanese and Dutch was compared, using a task in which listeners blended pseudo-word pairs such as ranga-serupa. An assimilated blend of this pair would be rampa, an unassimilated blend rangpa. Japanese listeners produced significantly more assimilated than unassimilated forms, both with pseudo-Japanese and pseudo-Dutch materials, while Dutch listeners produced significantly more unassimilated than assimilated forms in each materials set. This suggests that Japanese listeners, whose native-language phonology involves obligatory assimilation constraints, represent the assimilated nasals in nasal-stop sequences as unmarked for place of articulation, while Dutch listeners, who are accustomed to hearing unassimilated forms, represent the same nasal segments as marked for place of articulation.

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

A distinction between strong and weak vowels can be drawn on the basis of vowel quality, of stress, or of both factors. An experiment was conducted in which sets of contextually matched word-intial vowels ranging from clearly strong to clearly weak were cross-spliced, and the naturalness of the resulting words was rated by listeners. The ratings showed that in general cross-spliced words were only significantly less acceptable than unspliced words when schwa was not involved; this supports a categorical distinction based on vowel quality.

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

Languages contain tens of thousands of words, but these are constructed from a tiny handful of phonetic elements. Consequently, words resemble one another, or can be embedded within one another, a coup stick snot with standing. me process of spoken-word recognition by human listeners involves activation of multiple word candidates consistent with the input, and direct competition between activated candidate words. Further, human listeners are sensitive, at an early, prelexical, stage of speeeh processing, to constraints on what could potentially be a word of the language.

Abstract

The phoneme detection task is widely used in spoken word recognition research. Alphabetically literate participants, however, are more used to explicit representations of letters than of phonemes. The present study explored whether phoneme detection is sensitive to how target phonemes are, or may be, orthographically realised. Listeners detected the target sounds [b,m,t,f,s,k] in word-initial position in sequences of isolated English words. Response times were faster to the targets [b,m,t], which have consistent word-initial spelling, than to the targets [f,s,k], which are inconsistently spelled, but only when listeners’ attention was drawn to spelling by the presence in the experiment of many irregularly spelled fillers. Within the inconsistent targets [f,s,k], there was no significant difference between responses to targets in words with majority and minority spellings. We conclude that performance in the phoneme detection task is not necessarily sensitive to orthographic effects, but that salient orthographic manipulation can induce such sensitivity.

Abstract

Speakers and listeners have a shared goal: to communicate. The processes of speech perception and of speech production interact in many ways under the constraints of this communicative goal; such interaction is as characteristic of prosodic processing as of the processing of other aspects of linguistic structure. Two of the major uses of prosodic information in situations of communication are to encode salience and segmentation, and these themes unite the contributions to the symposium introduced by the present review.

Abstract

Continuers —words like mm, mmhm, uhum and the like— are among the most frequent types of responses in conversation. They play a key role in joint action coordination by showing positive evidence of understanding and scaffolding narrative delivery. Here we investigate the hypothesis that their functional importance along with their conversational ecology places selective pressures on their form and may lead to cross-linguistic similarities through convergent cultural evolution. We compare continuer tokens in linguistically diverse conversational corpora and find languages make available highly similar forms. We then approach the causal mechanism of convergent cultural evolution using exemplar modelling, simulating the process by which a combination of effort minimization and functional specialization may push continuers to a particular region of phonological possibility space. By combining comparative linguistics and computational modelling we shed new light on the question of how language structure is shaped by and for social interaction.

Abstract

Informal social interaction is the primordial home of human language. Linguistically diverse conversational corpora are an important and largely untapped resource for computational linguistics and language technology. Through the efforts of a worldwide language documentation movement, such corpora are increasingly becoming available. We show how interactional data from 63 languages (26 families) harbours insights about turn-taking, timing, sequential structure and social action, with implications for language technology, natural language understanding, and the design of conversational interfaces. Harnessing linguistically diverse conversational corpora will provide the empirical foundations for flexible, localizable, humane language technologies of the future.

Abstract

This paper summarizes the results of a descriptive syntactic category analysis of child English no which reveals that young children use and represent no as a determiner and negatives like no pen as NPs, contra standard analyses.

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

Like many Indigenous Australian languages, Murrinhpatha has flexible word order with no apparent configurational syntax. We analyzed an experimental corpus of Murrinhpatha utterances for associations between different thematic role orders, intonational phrasing patterns and pitch downtrends. We found that initial constituents (Agents or Patients) tend to carry the highest pitch targets (HiF0), followed by patterns of downstep and declination. Sentence-final verbs always have lower Hif0 values than either initial or medial Agents or Patients. Thematic role order does not influence intonational
patterns, with the results suggesting that Murrinhpatha has positional prosody, although final nominals can disrupt global
pitch downtrends regardless of thematic role.

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

Large pretrained language models (PreLMs) are rev-olutionizing natural language processing across all benchmarks. However, their sheer size is prohibitive for small laboratories or for deployment on mobile devices. Approaches like pruning and distillation reduce the model size but typically retain the same model architecture. In contrast, we explore distilling PreLMs into a different, more efficient architecture, Continual Multiplication of Words (CMOW), which embeds each word as a matrix and uses matrix multiplication to encode sequences. We extend the CMOW architecture and its CMOW/CBOW-Hybrid variant with a bidirectional component for more expressive power, per-token representations for a general (task-agnostic) distillation during pretraining, and a two-sequence encoding scheme that facilitates downstream tasks on sentence pairs, such as sentence similarity and natural language inference. Our matrix-based bidirectional CMOW/CBOW-Hybrid model is competitive to DistilBERT on question similarity and recognizing textual entailment, but uses only half of the number of parameters and is three times faster in terms of inference speed. We match or exceed the scores of ELMo for all tasks of the GLUE benchmark except for the sentiment analysis task SST-2 and the linguistic acceptability task CoLA. However, compared to previous cross-architecture distillation approaches, we demonstrate a doubling of the scores on detecting linguistic acceptability. This shows that matrix-based embeddings can be used to distill large PreLM into competitive models and motivates further research in this direction.

Abstract

Rhythm is a fundamental feature characterizing communicative displays, and recent studies showed that primate songs encompass categorical rhythms falling on small integer ratios observed in humans. We individually assessed the presence and sexual dimorphism of rhythmic categories, analyzing songs emitted by 39 wild indris. Considering the intervals between the units given during each song, we extracted 13556 interval ratios and found three peaks (at around 0.33, 0.47, and 0.70). Two peaks indicated rhythmic categories corresponding to small integer ratios (1:1, 2:1). All individuals showed a peak at 0.70, and
most showed those at 0.47 and 0.33. In addition, we found sex differences in the peak at 0.47 only, with males showing lower values than females. This work investigates the presence of individual rhythmic categories in a non-human species; further research may highlight the significance of rhythmicity and untie selective pressures that guided its evolution across species, including humans.

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

Humans differ greatly in their ability to use language. Contemporary psycholinguistic theories assume that individual differences in language skills arise from variability in linguistic experience and in general cognitive skills. While much previous research has tested the involvement of select verbal and non-verbal variables in select domains of linguistic processing, comprehensive characterizations of the relationships among the skills underlying language use are rare. We contribute to such a research program by re-analyzing a publicly available set of data from 112 young adults tested on 35 behavioral tests. The tests assessed nine key constructs reflecting linguistic processing skills, linguistic experience and general cognitive skills. Correlation and hierarchical clustering analyses of the test scores showed that most of the tests assumed to measure the same construct correlated moderately to strongly and largely clustered together. Furthermore, the results suggest important roles of processing speed in comprehension, and of linguistic experience in production.

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

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

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

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

Abstract

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

Response tokens (also known as backchannels, continuers, or feedback) are a frequent feature of human interaction, where they serve to display understanding and streamline turn-taking. We propose a bottom-up method to study responsive behaviour across 16 languages (8 language families). We use sequential context and recurrence of turns formats to identify candidate response tokens in a language-agnostic way across diverse conversational corpora. We then use UMAP clustering directly on speech signals to represent structure and variation. We find that (i) written orthographic annotations underrepresent the attested variation, (ii) distinctions between formats can be gradient rather than discrete, (iii) most languages appear to make available a broad distinction between a minimal nasal format `mm' and a fuller `yeah’-like format. Charting this aspect of human interaction contributes to our understanding of interactional infrastructure across languages and can inform the design of speech technologies.

Abstract

We present an analysis pipeline and best practice guidelines for building and curating corpora of everyday conversation in diverse languages. Surveying language documentation corpora and other resources that cover 67 languages and varieties from 28 phyla, we describe the compilation and curation process, specify minimal properties of a unified format for interactional data, and develop methods for quality control that take into account turn-taking and timing. Two case studies show the broad utility of conversational data for (i) charting human interactional infrastructure and (ii) tracing challenges and opportunities for current ASR solutions. Linguistically diverse conversational corpora can provide new insights for the language sciences and stronger empirical foundations for language technology.

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

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

Abstract

Distributional semantic models capture word-level meaning that is useful in many natural language processing tasks and have even been shown to capture cognitive aspects of word meaning. The majority of these models are purely text based, even though the human sensory experience is much richer. In this paper we create visually grounded word embeddings by combining English text and images and compare them to popular text-based methods, to see if visual information allows our model to better capture cognitive aspects of word meaning. Our analysis shows that visually grounded embedding similarities are more predictive of the human reaction times in a large priming experiment than the purely text-based embeddings. The visually grounded embeddings also correlate well with human word similarity ratings.Importantly, in both experiments we show that he grounded embeddings account for a unique portion of explained variance, even when we include text-based embeddings trained on huge corpora. This shows that visual grounding allows our model to capture information that cannot be extracted using text as the only source of information.

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

Gaze cues play an important role in human communication and are used to coordinate turn-taking and joint attention, as well as to regulate intimacy. In order to have fluent conversations with people, social robots need to exhibit humanlike gaze behavior. Previous Gaze Control Systems (GCS) in HRI have automated robot gaze using data-driven or heuristic approaches. However, these systems tend to be mainly reactive in nature. Planning the robot gaze ahead of time could help in achieving more realistic gaze behavior and better eye-head coordination. In this paper, we propose and implement a novel planning-based GCS. We evaluate our system in a comparative within-subjects user study (N=26) between a reactive system and our proposed system. The results show that the users preferred the proposed system and that it was significantly more interpretable and better at regulating intimacy.

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

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 speech involves repair. These repairs are often highlighted through prosodic marking (Levelt & Cutler, 1983). Prosodic marking usually entails an increase in pitch, loudness, and/or duration that draws attention to the corrected word. While it is established that natural self-repairs typically elicit prosodic marking, the exact cause of this is unclear. This study investigates whether producing a prosodic marking emerges from an automatic correction process or has a communicative purpose. In the current study, we elicit corrections to test whether all self-corrections elicit prosodic marking. Participants carried out a picture-naming task in which they described two images presented on-screen. To prompt self-correction, the second image was altered in some cases, requiring participants to abandon their initial utterance and correct their description to match the new image. This manipulation was compared to a control condition in which only the orientation of the object would change, eliciting no self-correction while still presenting a visual change. We found that the replacement of the item did not elicit a prosodic marking, regardless of the type of change. Theoretical implications and research directions are discussed, in particular theories of prosodic planning.

Abstract

Word count estimation (WCE) from audio recordings has a number of applications, including quantifying the amount of speech that language-learning infants hear in their natural environments, as captured by daylong recordings made with devices worn by infants. To be applicable in a wide range of scenarios and also low-resource domains, WCE tools should be extremely robust against varying signal conditions and require minimal access to labeled training data in the target domain. For this purpose, earlier work has used automatic syllabification of speech, followed by a least-squares-mapping of syllables to word counts. This paper compares a number of previously proposed syllabifiers in the WCE task, including a supervised bi-directional long short-term memory (BLSTM) network that is trained on a language for which high quality syllable annotations are available (a “high resource language”), and reports how the alternative methods compare on different languages and signal conditions. We also explore additive noise and varying-channel data augmentation strategies for BLSTM training, and show how they improve performance in both matching and mismatching languages. Intriguingly, we also find that even though the BLSTM works on languages beyond its training data, the unsupervised algorithms can still outperform it in challenging signal conditions on novel languages.

Abstract

It has been shown that when speakers produce hand gestures, addressees are attentive towards these gestures, using them to facilitate speech processing. Even relatively simple “beat” gestures are taken into account to help process aspects of speech such as prosodic prominence. In fact, recent evidence suggests that the timing of a beat gesture can influence spoken word recognition. Termed the manual McGurk Effect, Dutch participants, when presented with lexical stress minimal pair continua in Dutch, were biased to hear lexical stress on the syllable that coincided with a beat gesture. However, little is known about how this manual McGurk effect would surface in languages other than Dutch, with different acoustic cues to prominence, and variable gestures. Therefore, this study tests the effect in Spanish where lexical stress is arguably even more important, being a contrastive cue in the regular verb conjugation system. Results from 24 participants corroborate the effect in Spanish, namely that when given the same auditory stimulus, participants were biased to perceive lexical stress on the syllable that visually co-occurred with a beat gesture. These findings extend the manual McGurk effect to a different language, emphasizing the impact of gestures' timing on prosody perception and spoken word recognition.

Abstract

Recent research on multimodal language production has revealed that prominence in speech and gesture go hand-in-hand. Specifically, peaks in gesture (i.e., the apex) seem to closely coordinate with peaks in fundamental frequency (F0). The nature of this relationship may also be bi-directional, as it has also been shown that the production of gesture directly affects speech acoustics. However, most studies on the topic have largely focused on stress-based languages, where fundamental frequency has a prominence-lending function. Less work has been carried out on lexical tone languages such as Mandarin, where F0 is lexically distinctive. In this study, four native Mandarin speakers were asked to produce single monosyllabic CV words, taken from minimal lexical tone triplets (e.g., /pi1/, /pi2/, /pi3/), either with or without a beat gesture. Our analyses of the timing of the gestures showed that the gesture apex most stably occurred near vowel onset, with consonantal duration being the strongest predictor of apex placement. Acoustic analyses revealed that words produced with gesture showed raised F0 contours, greater intensity, and shorter durations. These findings further our understanding of gesture-speech alignment in typologically diverse languages, and add to the discussion about multimodal prominence.