Publications

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

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

The anterior lordosis of the cervical spine is thought
to contribute to pitch (fo) production by influencing
cricoid rotation as a function of larynx height. This
study examines the matter of inter-individual
variation in cervical spine shape and whether this has
an influence on how fo is produced along increasing
or decreasing scales, using the ArtiVarK dataset,
which contains real-time MRI pitch production data.
We find that the cervical spine actively participates in
fo production, but the amount of displacement
depends on individual shape. In general, anterior
spine motion (tending toward cervical lordosis)
occurs for low fo, while posterior movement (tending
towards cervical kyphosis) occurs for high fo.

Abstract

Recently, there has been an explosion in the availability of large, good-quality cross-linguistic databases such as WALS (Dryer & Haspelmath, 2013), Glottolog (Hammarstrom et al., 2015) and Phoible (Moran & McCloy, 2014). Databases such as Phoible contain the actual segments used by various languages as they are given in the primary language descriptions. However, this segment-level representation cannot be used directly for analyses that require generalizations over classes of segments that share theoretically interesting features. Here we present a method and the associated R (R Core Team, 2014) code that allows the exible denition of such meaningful classes and that can identify the sets of segments falling into such a class for any language inventory. The method and its results are important for those interested in exploring cross-linguistic patterns of phonetic and phonological diversity and their relationship to extra-linguistic factors and processes such as climate, economics, history or human genetics.

Abstract

The current paper presents results for data on click learning obtained from a larger imaging study (using MRI and 3D intraoral scanning) designed to quantify and characterize intra- and inter-population variation of vocal tract structures and the relation of this to speech production. The aim of the click study was to ascertain whether and to what extent vocal tract morphology influences (1) the ability to learn to produce clicks and (2) the productions of those that successfully learn to produce these sounds. The results indicate that the presence of an alveolar ridge certainly does not prevent an individual from learning to produce click sounds (1). However, the subtle details of how clicks are produced may indeed be driven by palate shape (2).

Abstract

In Iterated Learning (IL) experiments, a participant’s learned output serves as the next participant’s learning input (Kirby et al., 2014). IL can be used to model cultural transmission and has indicated that weak biases can be amplified through repeated cultural transmission (Kirby et al., 2007). So, for example, structural language properties can emerge over time because languages come to reflect the cognitive constraints in the individuals that learn and produce the language. Similarly, we propose that languages may also reflect certain anatomical biases. Do sound systems adapt to the affordances of the articulation space induced by the vocal tract?
The human vocal tract has inherent nonlinearities which might derive from acoustics and aerodynamics (cf. quantal theory, see Stevens, 1989) or biomechanics (cf. Gick & Moisik, 2015). For instance, moving the tongue anteriorly along the hard palate to produce a fricative does not result in large changes in acoustics in most cases, but for a small range there is an abrupt change from a perceived palato-alveolar [ʃ] to alveolar [s] sound (Perkell, 2012). Nonlinearities such as these might bias all human speakers to converge on a very limited set of phonetic categories, and might even be a basis for combinatoriality or phonemic ‘universals’.
While IL typically uses discrete symbols, Verhoef et al. (2014) have used slide whistles to produce a continuous signal. We conducted an IL experiment with human subjects who communicated using a digital slide whistle for which the degree of nonlinearity is controlled. A single parameter (α) changes the mapping from slide whistle position (the ‘articulator’) to the acoustics. With α=0, the position of the slide whistle maps Bark-linearly to the acoustics. As α approaches 1, the mapping gets more double-sigmoidal, creating three plateaus where large ranges of positions map to similar frequencies. In more abstract terms, α represents the strength of a nonlinear (anatomical) bias in the vocal tract.
Six chains (138 participants) of dyads were tested, each chain with a different, fixed α. Participants had to communicate four meanings by producing a continuous signal using the slide-whistle in a ‘director-matcher’ game, alternating roles (cf. Garrod et al., 2007).
Results show that for high αs, subjects quickly converged on the plateaus. This quick convergence is indicative of a strong bias, repelling subjects away from unstable regions already within-subject. Furthermore, high αs lead to the emergence of signals that oscillate between two (out of three) plateaus. Because the sigmoidal spaces are spatially constrained, participants increasingly used the sequential/temporal dimension. As a result of this, the average duration of signals with high α was ~100ms longer than with low α. These oscillations could be an expression of a basis for phonemic combinatoriality.
We have shown that it is possible to manipulate the magnitude of an articulator-induced non-linear bias in a slide whistle IL framework. The results suggest that anatomical biases might indeed constrain the design space of language. In particular, the signaling systems in our study quickly converged (within-subject) on the use of stable regions. While these conclusions were drawn from experiments using slide whistles with a relatively strong bias, weaker biases could possibly be amplified over time by repeated cultural transmission, and likely lead to similar outcomes.

Abstract

Many factors have been proposed to explain why groups of people use different speech sounds in their language. These range from cultural, cognitive, environmental (e.g., Everett, et al., 2015) to anatomical (e.g., vocal tract (VT) morphology). How could such anatomical properties have led to the similarities and differences in speech sound distributions between human languages?

It is known that hard palate profile variation can induce different articulatory strategies in speakers (e.g., Brunner et al., 2009). That is, different hard palate profiles might induce a kind of bias on speech sound production, easing some types of sounds while impeding others. With a population of speakers (with a proportion of individuals) that share certain anatomical properties, even subtle VT biases might become expressed at a population-level (through e.g., bias amplification, Kirby et al., 2007). However, before we look into population-level effects, we should first look at within-individual anatomical factors. For that, we have developed a computer-simulated analogue for a human speaker: an agent. Our agent is designed to replicate speech sounds using a production and cognition module in a computationally tractable manner.

Previous agent models have often used more abstract (e.g., symbolic) signals. (e.g., Kirby et al., 2007). We have equipped our agent with a three-dimensional model of the VT (the production module, based on Birkholz, 2005) to which we made numerous adjustments. Specifically, we used a 4th-order Bezier curve that is able to capture hard palate variation on the mid-sagittal plane (XXX, 2015). Using an evolutionary algorithm, we were able to fit the model to human hard palate MRI tracings, yielding high accuracy fits and using as little as two parameters. Finally, we show that the samples map well-dispersed to the parameter-space, demonstrating that the model cannot generate unrealistic profiles. We can thus use this procedure to import palate measurements into our agent’s production module to investigate the effects on acoustics. We can also exaggerate/introduce novel biases.

Our agent is able to control the VT model using the cognition module.

Previous research has focused on detailed neurocomputation (e.g., Kröger et al., 2014) that highlights e.g., neurobiological principles or speech recognition performance. However, the brain is not the focus of our current study. Furthermore, present-day computing throughput likely does not allow for large-scale deployment of these architectures, as required by the population model we are developing. Thus, the question whether a very simple cognition module is able to replicate sounds in a computationally tractable manner, and even generalize over novel stimuli, is one worthy of attention in its own right.

Our agent’s cognition module is based on running an evolutionary algorithm on a large population of feed-forward neural networks (NNs). As such, (anatomical) bias strength can be thought of as an attractor basin area within the parameter-space the agent has to explore. The NN we used consists of a triple-layered (fully-connected), directed graph. The input layer (three neurons) receives the formants frequencies of a target-sound. The output layer (12 neurons) projects to the articulators in the production module. A hidden layer (seven neurons) enables the network to deal with nonlinear dependencies. The Euclidean distance (first three formants) between target and replication is used as fitness measure. Results show that sound replication is indeed possible, with Euclidean distance quickly approaching a close-to-zero asymptote.

Statistical analysis should reveal if the agent can also: a) Generalize: Can it replicate sounds not exposed to during learning? b) Replicate consistently: Do different, isolated agents always converge on the same sounds? c) Deal with consolidation: Can it still learn new sounds after an extended learning phase (‘infancy’) has been terminated? Finally, a comparison with more complex models will be used to demonstrate robustness.

Abstract

We introduce a biomechanical model of oropharyngeal structures that adds the soft-palate, pharynx, and larynx to our previous models of jaw, skull, hyoid, tongue, and face in a unified model. The model includes a comprehensive description of the upper airway musculature, using point-to-point muscles that may either be embedded within the deformable structures or operate exter- nally. The airway is described by an air-tight mesh that fits and deforms with the surrounding articulators, which enables dynamic coupling to our articulatory speech synthesizer. We demonstrate that the biomechanics, in conjunction with the skinning, supports a range from physically realistic to simplified vocal tract geometries to investigate different approaches to aeroacoustic modeling of vocal tract. Furthermore, our model supports inverse modeling to support investigation of plausible muscle activation patterns to generate speech.

Abstract

The larynx is the essential articulatory mechanism that primes the vocal tract. Far from being only a glottal source of voicing, the complex laryngeal mechanism entrains the ontogenetic acquisition of speech and, through coarticulatory coupling, guides the production of oral sounds in the infant vocal tract. As such, it is not possible to speculate as to the origins of the speaking modality in humans without considering the fundamental role played by the laryngeal articulatory mechanism. The Laryngeal Articulator Model, which divides the vocal tract into a laryngeal component and an oral component, serves as a basis for describing early infant speech and for positing how speech sounds evolving in various hominids may be related phonetically. To this end, we offer some suggestions for how the evolution and development of vocal tract anatomy fit with our infant speech acquisition data and discuss the implications this has for explaining phonetic learning and for interpreting the biological evolution of the human vocal tract in relation to speech and speech acquisition.

Abstract

The human hard palate shows between-subject variation
that is known to influence articulatory strategies.
In order to link such variation to human speech, we
are conducting a cross-sectional MRI study on multiple
populations. A model based on Bezier curves
using only three parameters was fitted to hard palate
MRI tracings using evolutionary computation. The
fits produced consistently yield high accuracies. For
future research, this new method may be used to classify
our MRI data on ethnic origins using e.g., cluster
analyses. Furthermore, we may integrate our model
into three-dimensional representations of the vocal
tract in order to investigate its effect on acoustics and
cultural transmission.

Abstract

It has been observed by several researchers that the Khoisan palate tends to lack a prominent alveolar ridge. A preliminary biomechanical model of click production was created to examine if these sounds might be subject to an anatomical bias associated with alveolar ridge size. Results suggest the bias is plausible, taking the form of decreased articulatory effort and improved volume change characteristics, however, further modelling and experimental research is required to solidify the claim.

Abstract

Dediu and Levinson (2013) argue that Neandertals had essentially modern language and speech, and that they were in genetic contact with the ancestors of modern humans during our dispersal out of Africa. This raises the possibility of cultural and linguistic contact between the two human lineages. If such contact did occur, then it might have influenced the cultural evolution of the languages. Since the genetic traces of contact with Neandertals are limited to the populations outside of Africa, Dediu & Levinson predict that there may be structural differences between the present-day languages derived from languages in contact with Neanderthals, and those derived from languages that were not influenced by such contact. Since the signature of such deep contact might reside in patterns of features, they suggested that machine learning methods may be able to detect these differences. This paper attempts to test this hypothesis and to estimate particular linguistic features that are potential candidates for carrying a signature of Neandertal languages.