Publications

Abstract

In this work we perform multimodal detection and classification
of head movements from face to face video conversation data.
We have experimented with different models and feature sets
and provided some insight on the effect of independent features,
but also how their interaction can enhance a head movement
classifier. Used features include nose, neck and mid hip position
coordinates and their derivatives together with acoustic features,
namely, intensity and pitch of the speaker on focus. Results
show that when input features are sufficiently processed by in-
teracting with each other, a linear classifier can reach a similar
performance to a more complex non-linear neural model with
several hidden layers. Our best models achieve state-of-the-art
performance in the detection task, measured by macro-averaged
F1 score.

Abstract

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

Abstract

The 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

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

Abstract

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

Narrow focus refers to accent placement that forces one interpretation of a sentence, which is then often perceived contrastively. Narrow focus is formalised in terms of alternative sets, i.e. contextually or situationally salient alternatives. In this paper, we investigate whether this model is valid also in human utterance processing. We present an eye-tracking experiment to study listeners’ expectations (i.e. eye-movements) with respect to upcoming referents. Some of the objects contrast in colour with objects that were previously referred to, others do not; the objects are referred to with either a narrow focus on the colour adjective or with broad focus on the noun. Results show that narrow focus on the adjective increases early fixations to contrastive referents. Narrow focus hence activates alternative referents in human utterance processing

Abstract

In order to understand early word learning we need to better understand and quantify properties of the input that young children receive. We extended the human simulation paradigm (HSP) using egocentric videos taken from infant head-mounted cameras. The videos were further annotated with gaze information indicating in-the-moment visual attention from the infant. Our new HSP prompted participants for two types of responses, thus differentiating referential from semantic ambiguity in the learning input. Consistent with findings on visual attention in word learning, we find a strongly bimodal distribution over HSP accuracy. Even in this open-ended task, most videos only lead to a small handful of common responses. What's more, referential ambiguity was the key bottleneck to performance: participants can nearly always recover the exact word that was said if they identify the correct referent. Finally, analysis shows that adult learners relied on particular, multimodal behavioral cues to infer those target referents.

Abstract

The present study set out to pin down the role of four pitch accents, fall (H*L), rise-fall (L*HL), rise (L*H), fall-rise (H*LH), as well as deaccentuation, in interpreting new vs. given information in British English by the eyetracking paradigm. The pitch accents in question were claimed to convey information status in theories of English intonational meaning. There is, however, no consensus on the postulated roles of these pitch accents. Results clearly show that pitch accent type can and does matter when interpreting information status. The effects can be reflected in the mean proportions of fixations to the competitor in a selected time window. These patterns are also present in proportions of fixations to the target but to a lesser extent. Interestingly, the effects of pitch accent types are also reflected in how fast the participants could adjust their decision as to which picture to move before the name of the picture was fully revealed. For example, when the competitor was a given entity, the proportion of fixations to the competitor increased initially in most accent conditions in the first as a result of subjects' bias towards a given entity, but started to decrease substantially earlier in the H*L condition than in the L*H and deaccentuation conditions.

Abstract

This study investigates how Mandarin-speaking children interpret state-change verbs. In Mandarin, state-change is typically encoded with resultative verb compounds (RVCs), in which the first verb (V1) specifies an action and the second (V2) a result, for example, zhai-xia 'pick-descend' (= pick, pick off/down). Unlike English state-change verb such as pick, smash, mix and fill, the action verb (V1) may imply a state-change but it does not entail it; the state-change is specified by the additional result verb (V2). Previous studies have shown that children learning English and German tend to neglect the state-change meaning in monomorphemic state-change verbs like mix and fill (Gentner, 1978; Gropen et al, 1991) and verb-particle constructions like abplücken 'pick off' (Wittek, 1999, 2000) - they do not realize that this meaning is entailed. This study examines how Mandarin-speaking children interpret resultative verb compounds and the first verb of an RVC. Four groups of Mandarin-speaking children (mean ages 2;6, 3;6, 4;6, 6;1) and an adult group participated in a judgment task. The results show that Mandarin-speaking children know from a very young age that RVCs entail a state-change; ironically, however, they make a mistake that is just the opposite to that made by the learners of English and German: they often incorrectly interpret the action verb (V1) of an RVC as if it, in itself, also entails a state-change, even though it does not. This result suggests that children do not have a uniform strategy for interpreting verb meaning, but are influenced by the language-specific lexicalization patterns they encounter in their language.

Abstract

In this study, we analyze one French signing and one French speaking family’s interaction during dinner. The families composed of two parents and two children aged 3 to 11 were filmed with three cameras to capture all family members’ behaviors. The three videos per dinner were synchronized and coded on ELAN. We annotated all participants’ acting, and languaging.
Our quantitative analyses show how family members collaboratively manage multiple streams of activity through the embodied performances of dining and interacting. We uncover different profiles according to participants’ modality of expression and status (focusing on the mother and the younger child). The hearing participants’ co-activity management illustrates their monitoring of dining and conversing and how they progressively master the affordances of the visual and vocal channels to maintain the simultaneity of the two activities. The deaf mother skillfully manages to alternate smoothly between dining and interacting. The deaf younger child manifests how she is in the process of developing her skills to manage multi-activity. Our qualitative analyses focus on the ecology of visual-gestural and audio-vocal languaging in the context of co-activity according to language and participant. We open new perspectives on the management of gaze and body parts in multimodal languaging.

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

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

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

Abstract

This paper 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

The study presented here focuses on two pragmatic gestures:
the hand flip (Ferré, 2011), a gesture of the Palm Up Open
Hand/PUOH family (Müller, 2004) and the closed hand which
can be considered as the opposite kind of movement to the open-
ing of the hands present in the PUOH gesture. Whereas one of
the functions of the hand flip has been described as presenting
a new point in speech (Cienki, 2021), the closed hand gesture
has not yet been described in the literature to the best of our
knowledge. It can however be conceived of as having the oppo-
site function of announcing the end of a point in discourse. The
object of the present study is therefore to determine, with the
study of prosodic features, if the two gestures are found in the
same type of speech units and what their respective scope is.
Drawing from a corpus of three TED Talks in French the
prosodic characteristics of the speech that accompanies the two
gestures will be examined. The hypothesis developed in the
present paper is that their scope should be reflected in the
prosody of accompanying speech, especially pitch key, tone,
and relative pitch range. The prediction is that hand flips and
closing hand gestures are expected to be located at the periph-
ery of Intonation Phrases (IPs), Inter-Pausal Units (IPUs) or
more conversational Turn Constructional Units (TCUs), and are
likely to be co-occurrent with pauses in speech. But because of
the natural slope of intonation in speech, the speech that accom-
pany early gestures in Intonation Phrases should reveal different
features from the speech at the end of intonational units. Tones
should be different as well, considering the prosodic structure
of spoken French.

Abstract

In this paper we investigate the neural correlates of syntactic classification of an acquired grammatical sequence structure in an event-related FMRI study. During acquisition, participants were engaged in an implicit short-term memory task without performance feedback. We manipulated the statistical frequency-based and rule-based characteristics of the classification stimuli independently in order to investigate their role in artificial grammar acquisition. The participants performed reliably above chance on the classification task. We observed a partly overlapping corticostriatal processing network activated by both manipulations including inferior prefrontal, cingulate, inferior parietal regions, and the caudate nucleus. More specifically, the left inferior frontal BA 45 and the caudate nucleus were sensitive to syntactic violations and endorsement, respectively. In contrast, these structures were insensitive to the frequency-based manipulation.

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

The last few years have revealed that several species may share the building blocks of Musicality with humans. The recognition of these building blocks (e.g., rhythm, frequency variation) was a necessary impetus for a new round of studies investigating rhythmic variation in animal vocal displays. Singing primates are a small group of primate species that produce modulated songs ranging from tens to thousands of vocal units. Previous studies showed that the indri, the only singing lemur, is currently the only known species that perform duet and choruses showing multiple rhythmic categories, as seen in human music. Rhythmic categories occur when temporal intervals between note onsets are not uniformly distributed, and rhythms with a small integer ratio between these intervals are typical of human music. Besides indris, white-handed gibbons and three crested gibbon species showed a prominent rhythmic category corresponding to a single small integer ratio, isochrony. This study reviews previous evidence on the co-occurrence of rhythmic categories in primates and focuses on the prospects for a comparative, multimodal study of rhythmicity in this clade.

Abstract

Despite the early emergence of pointing, children are generally not documented to produce iconic gestures until later in development. Although research has described this developmental trajectory and the types of iconic gestures that emerge first, there has been limited focus on iconic gestures within interactional contexts. This study identified the first 10 iconic gestures produced by five monolingual English-speaking children in a naturalistic longitudinal video corpus and analysed the interactional contexts. We found children produced their first iconic gesture between 12 and 20 months and that gestural types varied. Although 34% of gestures could have been imitated or derived from adult or child actions in the preceding context, the majority were produced independently of any observed model. In these cases, adults often led the interaction in a direction where iconic gesture was an appropriate response. Overall, we find infants can represent a referent symbolically and possess a greater capacity for innovation than previously assumed. In order to develop our understanding of how children learn to produce iconic gestures, it is important to consider the immediate interactional context. Conducting naturalistic corpus analyses could be a more ecologically valid approach to understanding how children learn to produce iconic gestures in real life contexts.

Abstract

Face to face interaction is fundamental to human sociality but is very complex to study in a scientific fashion. This theme issue brings together cutting-edge approaches to the study of face-to-face interaction and showcases how we can make progress in this area. Researchers are now studying interaction in adult conversation, parent-child relationships, neurodiverse groups, interactions with virtual agents and various animal species. The theme issue reveals how new paradigms are leading to more ecologically grounded and comprehensive insights into what social interaction is. Scientific advances in this area can lead to improvements in education and therapy, better understanding of neurodiversity and more engaging artificial agents

Abstract

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

Abstract

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

Abstract

This special publication aims to build a renewed enthusiasm for collecting acquisition data across many languages, including those facing endangerment and loss. It presents a guide for documenting and describing child language and child-directed language in diverse languages and cultures, as well as a collection of acquisition sketches based on this guide. The guide is intended for anyone interested in working across child language and language documentation, including, for example, field linguists and language documenters, community language workers, child language researchers or graduate students.

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

Most bioacoustics software is used to analyse the already collected acoustics data in batch, i.e., after the data-collecting phase of a scientific study. However, experiments based on animal training require immediate and precise reactions from the experimenter, and thus do not easily dovetail with a typical bioacoustics workflow. Bridging this methodological gap, we have developed a custom application to live-monitor the vocal development of harbour seals in a behavioural experiment. In each trial, the application records and automatically detects an animal's call, and immediately measures duration and acoustic measures such as intensity, fundamental frequency, or formant frequencies. It then displays a spectrogram of the recording and the acoustic measurements, allowing the experimenter to instantly evaluate whether or not to reinforce the animal's vocalisation. From a technical perspective, the rapid and easy development of this custom software was made possible by combining multiple open-source software projects. Here, we integrated the acoustic analyses from Parselmouth, a Python library for Praat, together with PyAudio and Matplotlib's recording and plotting functionality, into a custom graphical user interface created with PyQt. This flexible recombination of different open-source Python libraries allows the whole program to be written in a mere couple of hundred lines of code

Abstract

This study investigates the time-course of information processing in both visual as well as in the auditory speech as used for word identification in face-to-face communication. It extends the limited previous research on this topic and provides a valuable database for future research in audiovisual speech perception. An evaluation of models of speech perception by ear and eye in their ability to account for the audiovisual gating data shows a superior role of the fuzzy logical model of perception (FLMP) [1] over additive models of perception. A new dynamic version of the FLMP seems to be a promising model to account for the complex interplay of perceptual and cognitive information in audiovisual spoken word recognition.

Abstract

Mutations of the epidermal growth factor receptor (EGFR) gene are found at a relatively high frequency in glioma, with the most common being the de2-7 EGFR (or EGFRvIII). This mutation arises from an in-frame deletion of exons 2–7, which removes 267 amino acids from the extracellular domain of the receptor. Despite being unable to bind ligand, the de2-7 EGFR is constitutively active at a low level. Transfection of human glioma cells with the de2-7 EGFR has little effect in vitro, but when grown as tumor xenografts this mutated receptor imparts a dramatic growth advantage. We have now mapped the phosphorylation pattern of de2-7 EGFR, both in vivo and in vitro, using a panel of antibodies unique to the different phosphorylated tyrosine residues. Phosphorylation of de2-7 EGFR was detected constitutively at all tyrosine sites surveyed both in vitro and in vivo, including tyrosine 845, a known target in the wild-type EGFR for src kinase. There was a substantial upregulation of phosphorylation at every tyrosine residue of the de2-7 EGFR when cells were grown in vivo compared to the receptor isolated from cells cultured in vitro. Upregulation of phosphorylation could be mimicked in vitro by the addition of specifi c components of the ECM such as collagen via an integrin-dependent mechanism. Since this increase in in vivo phosphorylation enhances de2-7 EGFR signaling, this observation explains why the growth enhancement mediated by de2-7 EGFR is largely restricted to the in vivo environment. In a second set of experiments we analyzed the interaction between EGFRvIII and ErbB2. Co-expression of these proteins in NR6 cells, a mouse fi broblast line devoid of ErbB family members, dramatically enhanced in vivo tumorigenicity of these cells compared to cells expressing either protein alone. Detailed analysis of these xenografts demonstrated that EGFRvIII could heterodimerize and transphosphorylate the ErbB2. Since both EGFRvIII and ErbB2 are commonly expressed at gliomas, this data suggests that the co-expression of these two proteins may enhance glioma tumorigenicity.

Abstract

Sign language is a rich form of communication, uniquely conveying meaning through a combination of gestures, facial expressions, and body movements. Existing research in sign language generation has predominantly focused on text-to-sign pose generation, while speech-to-sign pose generation remains relatively underexplored. Speech-to-sign language generation models can facilitate effective communication between the deaf and hearing communities. In this paper, we propose an architecture that utilises prosodic information from speech audio and semantic context from text to generate sign pose sequences. In our approach, we adopt a multi-tasking strategy that involves an additional task of predicting Facial Action Units (FAUs). FAUs capture the intricate facial muscle movements that play a crucial role in conveying specific facial expressions during sign language generation. We train our models on an existing Indian Sign language dataset that contains sign language videos with audio and text translations. To evaluate our models, we report Dynamic Time Warping (DTW) and Probability of Correct Keypoints (PCK) scores. We find that combining prosody and text as input, along with incorporating facial action unit prediction as an additional task, outperforms previous models in both DTW and PCK scores. We also discuss the challenges and limitations of speech-to-sign pose generation models to encourage future research in this domain. We release our models, results and code to foster reproducibility and encourage future research1.

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

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

Abstract

The 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

This paper examines the language-specific aspect of the TIME PASSING IS MOTION metaphor and suggests that the temporal construal of time can be influenced by a person's second language. Ahrens and Huang (2002) have analyzed the source domain of MOTION for the TIME metaphor into two special cases. In the special case one, TIME PASSING is an object that moves towards an ego. For example, qimuokao kuai dao le "the final exam is approaching." In the special case two, TIME PASSING is a point (that a plural ego is attached to) that moves across a landscape. For example, women kuai dao qimuokao le "we are approaching the final exam." In addition, in English, the ego in the special case one faces the future while in Chinese, the ego faces the past. The current experiment hypothesizes that English influences the choice of the orientation of the ego in native Chinese speakers who speak English as the second language. 54 subjects are asked to switch the clock time one hour forward. Results show that native Chinese speakers living in the Chinese speaking country tend to move the clock one hour forward to the past (92%) while native Chinese speakers living in an English speaking country are less likely to do so (60%). This implies that the experience of English influences the conceptualization of time in Mandarin Chinese.

Abstract

Interpreting the meaning of co-speech gesture often involves
identifying a gesture’s ‘lexical affiliate’, the word or phrase to
which it most closely relates (Schegloff 1984). Though there is
work within gesture studies that resists this simplex mapping of
meaning from speech to gesture (e.g. de Ruiter 2000; Kendon
2014; Parrill 2008), including an evolving body of literature on
recurrent gesture and gesture families (e.g. Fricke et al. 2014; Müller 2017), it is still the lexical affiliate model that is most ap-
parent in formal linguistic models of multimodal meaning(e.g.
Alahverdzhieva et al. 2017; Lascarides and Stone 2009; Puste-
jovsky and Krishnaswamy 2021; Schlenker 2020). In this work,
I argue that the lexical affiliate should be carefully reconsidered
in the further development of such models.
In place of the lexical affiliate, I suggest a further shift
toward a frame-based, action schematic approach to gestural
meaning in line with that proposed in, for example, Parrill and
Sweetser (2004) and Müller (2017). To demonstrate the utility
of this approach I present three types of compositional gesture
sequences which I call spatial contrast, spatial embedding, and
cooperative abstract deixis. All three rely on gestural context,
rather than gesture-speech alignment, to convey interactive (i.e.
pragmatic) meaning. The centrality of gestural context to ges-
ture meaning in these examples demonstrates the necessity of
developing a model of gestural meaning independent of its in-
tegration with speech.

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

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

Abstract

Large language models that exhibit instruction-following behaviour represent one of the biggest recent upheavals in conversational interfaces, a trend in large part fuelled by the release of OpenAI's ChatGPT, a proprietary large language model for text generation fine-tuned through reinforcement learning from human feedback (LLM+RLHF). We review the risks of relying on proprietary software and survey the first crop of open-source projects of comparable architecture and functionality. The main contribution of this paper is to show that openness is differentiated, and to offer scientific documentation of degrees of openness in this fast-moving field. We evaluate projects in terms of openness of code, training data, model weights, RLHF data, licensing, scientific documentation, and access methods. We find that while there is a fast-growing list of projects billing themselves as 'open source', many inherit undocumented data of dubious legality, few share the all-important instruction-tuning (a key site where human labour is involved), and careful scientific documentation is exceedingly rare. Degrees of openness are relevant to fairness and accountability at all points, from data collection and curation to model architecture, and from training and fine-tuning to release and deployment.

Abstract

Speech recognition systems are a key intermediary in voice-driven human-computer interaction. Although speech recognition works well for pristine monologic audio, real-life use cases in open-ended interactive settings still present many challenges. We argue that timing is mission-critical for dialogue systems, and evaluate 5 major commercial ASR systems for their conversational and multilingual support. We find that word error rates for natural conversational data in 6 languages remain abysmal, and that overlap remains a key challenge (study 1). This impacts especially the recognition of conversational words (study 2), and in turn has dire consequences for downstream intent recognition (study 3). Our findings help to evaluate the current state of conversational ASR, contribute towards multidimensional error analysis and evaluation, and identify phenomena that need most attention on the way to build robust interactive speech technologies.

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

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

Abstract

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

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

Abstract

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

Abstract

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

Abstract

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

Many studies investigating the temporal alignment of co-speech
gestures to acoustic units in the speech signal find a close
coupling of the gestural landmarks and pitch accents or the
stressed syllable of pitch-accented words. In English, a pitch
accent is anchored in the lexically stressed syllable. Hence, it is
unclear whether it is the lexical phonological dimension of
stress, or the phrase-level prominence that determines the
details of speech-gesture synchronization. This paper explores
the relation between gestural phase transitions and accentual F0
peaks in Stockholm Swedish, which exhibits a lexical pitch
accent distinction. When produced with phrase-level
prominence, there are three different configurations of
lexicality of F0 peaks and the status of the syllable it is aligned
with. Through analyzing the alignment of the different F0 peaks
with gestural onsets in spontaneous dyadic conversations, we
aim to contribute to our understanding of the role of lexical
prosodic phonology in the co-production of speech and gesture.
The results, though limited by a small dataset, still suggest
differences between the three types of peaks concerning which
types of gesture phase onsets they tend to align with, and how
well these landmarks align with each other, although these
differences did not reach significance.

Abstract

Phonetic convergence—i.e., adapting one’s speech
towards that of an interlocutor—has been shown
to occur in human-human conversations as well as
human-machine interactions. Here, we investigate
the hypothesis that human-to-robot convergence is
influenced by the human’s perception of the robot
and by the conversation’s topic. We conducted a
within-subjects experiment in which 33 participants
interacted with two robots differing in their eye gaze
behavior—one looked constantly at the participant;
the other produced gaze aversions, similarly to a
human’s behavior. Additionally, the robot asked
questions with increasing intimacy levels.
We observed that the speakers tended to converge
on F0 to the robots. However, this convergence
to the robots was not modulated by how the
speakers perceived them or by the topic’s intimacy.
Interestingly, speakers produced lower F0 means
when talking about more intimate topics. We
discuss these findings in terms of current theories of
conversational convergence.