A Lexical Comparison of Signs from Icelandic and Danish Sign Languages

Aldersson, R.; McEntee-Atalianis, Lisa

doi:10.1353/sls.0.0007

Cited by 15 publications

(10 citation statements)

References 11 publications

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“…In particular, using traditional historical comparative methods, sign researchers have been unable to distinguish the results of tree-compatible evolutionary processes-that is, patterns of similarity reflecting inheritance in a vertical ancestor-descendant relationship-from tree-incompatible processes, such as borrowing and convergence. As a consequence of these methodological challenges, comparative studies of SLs at times conflate vertical and horizontal relationships in forming SL families [37] or these studies forgo historical interpretations of their results [38].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary dynamics in the dispersal of sign languages

Power

Grimm²,

List

2020

R. Soc. open sci.

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The evolution of spoken languages has been studied since the mid-nineteenth century using traditional historical comparative methods and, more recently, computational phylogenetic methods. By contrast, evolutionary processes resulting in the diversity of contemporary sign languages (SLs) have received much less attention, and scholars have been largely unsuccessful in grouping SLs into monophyletic language families using traditional methods. To date, no published studies have attempted to use language data to infer relationships among SLs on a large scale. Here, we report the results of a phylogenetic analysis of 40 contemporary and 36 historical SL manual alphabets coded for morphological similarity. Our results support grouping SLs in the sample into six main European lineages, with three larger groups of Austrian, British and French origin, as well as three smaller groups centring around Russian, Spanish and Swedish. The British and Swedish lineages support current knowledge of relationships among SLs based on extra-linguistic historical sources. With respect to other lineages, our results diverge from current hypotheses by indicating (i) independent evolution of Austrian, French and Spanish from Spanish sources; (ii) an internal Danish subgroup within the Austrian lineage; and (iii) evolution of Russian from Austrian sources.

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Section: Introductionmentioning

confidence: 99%

Evolutionary dynamics in the dispersal of sign languages

Power

Grimm²,

List

2020

R. Soc. open sci.

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“…"Faux Amis". There are works investigating lexical similarities between sign languages manually [58,59]. We show qualitatively the potential of our model to discover similarities, as well as "faux-amis" between different sign languages, The predicted locations of the signs correspond to the peak similarity scores.…”

Section: Applicationsmentioning

confidence: 64%

Watch, read and lookup: learning to spot signs from multiple supervisors

Momeni¹,

Varol²,

Albanie³

et al. 2020

Preprint

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The focus of this work is sign spotting-given a video of an isolated sign, our task is to identify whether and where it has been signed in a continuous, co-articulated sign language video. To achieve this sign spotting task, we train a model using multiple types of available supervision by: (1) watching existing sparsely labelled footage; (2) reading associated subtitles (readily available translations of the signed content) which provide additional weak-supervision; (3) looking up words (for which no co-articulated labelled examples are available) in visual sign language dictionaries to enable novel sign spotting. These three tasks are integrated into a unified learning framework using the principles of Noise Contrastive Estimation and Multiple Instance Learning. We validate the effectiveness of our approach on low-shot sign spotting benchmarks. In addition, we contribute a machine-readable British Sign Language (BSL) dictionary dataset of isolated signs, BslDict, to facilitate study of this task. The dataset, models and code are available at our project page 1 .Equal contribution 1 https://www.robots.ox.ac.uk/ ~vgg/research/bsldict/

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“…"Faux Amis". There are works investigating lexical similarities between sign languages manually [6,52]. We show qualitatively the potential of our model to discover similarities, as well as "faux-amis" between different sign languages, in particular between British (BSL) and American (ASL) Sign Languages.…”

Section: Applicationsmentioning

confidence: 68%

Scaling up sign spotting through sign language dictionaries

Varol,

Momeni,

Albanie

et al. 2022

Preprint

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The focus of this work is sign spottinggiven a video of an isolated sign, our task is to identify whether and where it has been signed in a continuous, co-articulated sign language video. To achieve this sign spotting task, we train a model using multiple types of available supervision by: (1) watching existing footage which is sparsely labelled using mouthing cues;(2) reading associated subtitles (readily available translations of the signed content) which provide additional weak-supervision; (3) looking up words (for which no co-articulated labelled examples are available) in visual sign language dictionaries to enable novel sign spotting. These three tasks are integrated into a unified learning framework using the principles of Noise Contrastive Estimation and Multiple Instance Learning. We validate the effectiveness of our approach on low-shot sign spotting benchmarks. In addition, we contribute a machine-readable British Sign Language (BSL) dictionary dataset of isolated signs, BslDict, to facilitate study of this task. The dataset, models and code are available at our project page.

show abstract

A Lexical Comparison of Signs from Icelandic and Danish Sign Languages

Cited by 15 publications

References 11 publications

Evolutionary dynamics in the dispersal of sign languages

Evolutionary dynamics in the dispersal of sign languages

Watch, read and lookup: learning to spot signs from multiple supervisors

Scaling up sign spotting through sign language dictionaries

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