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How does sign language compare to gesture, on the one hand, and to spoken language on the other? At one time, sign was viewed as nothing more than a system of pictorial gestures with no linguistic structure. More recently, researchers have argued that sign is no different from spoken language with all of the same linguistic structures. The pendulum is currently swinging back toward the view that sign is gestural, or at least has gestural components. The goal of this review is to elucidate the relationships among sign language, gesture, and spoken language. We do so by taking a close look not only at how sign has been studied over the last 50 years, but also at how the spontaneous gestures that accompany speech have been studied. We come to the conclusion that signers gesture just as speakers do. Both produce imagistic gestures along with more categorical signs or words. Because, at the moment, it is difficult to tell where sign stops and where gesture begins, we suggest that sign should not be compared to speech alone, but should be compared to speech-plus-gesture. Although it might be easier (and, in some cases, preferable) to blur the distinction between sign and gesture, we argue that making a distinction between sign (or speech) and gesture is essential to predict certain types of learning, and allows us to understand the conditions under which gesture takes on properties of sign, and speech takes on properties of gesture. We end by calling for new technology that may help us better calibrate the borders between sign and gesture.

Sign languages display remarkable crosslinguistic consistencies in the use of handshapes. In particular, handshapes used in classifier predicates display a consistent pattern in finger complexity: classifier handshapes representing objects display more finger complexity than those representing how objects are handled. Here we explore the conditions under which this morphophonological phenomenon arises. In Study 1, we ask whether hearing individuals in Italy and the United States, asked to communicate using only their hands, show the same pattern of finger complexity found in the classifier handshapes of two sign languages: Italian Sign Language (LIS) and American Sign Language (ASL). We find that they do not: gesturers display more finger complexity in handling handshapes than in object handshapes. The morphophonological pattern found in conventional sign languages is therefore not a codified version of the pattern invented by hearing individuals on the spot. In Study 2, we ask whether continued use of gesture as a primary communication system results in a pattern that is more similar to the morphophonological pattern found in conventional sign languages or to the pattern found in gesturers. Homesigners have not acquired a signed or spoken language and instead use a self-generated gesture system to communicate with their hearing family members and friends. We find that homesigners pattern more like signers than like gesturers: their finger complexity in object handshapes is higher than that of gesturers (indeed as high as signers); and their finger complexity in handling handshapes is lower than that of gesturers (but not quite as low as signers). Generally, our findings indicate two markers of the phonologization of handshape in sign languages: increasing finger complexity in object handshapes, and decreasing finger complexity in handling handshapes. These first indicators of phonology appear to be present in individuals developing a gesture system without benefit of a linguistic community. Finally, we propose that iconicity, morphology and phonology each play an important role in the system of sign language classifiers to create the earliest markers of phonology at the morphophonological interface.

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We address the problem of American Sign Language fingerspelling recognition "in the wild", using videos collected from websites. We introduce the largest data set available so far for the problem of fingerspelling recognition, and the first using naturally occurring video data. Using this data set, we present the first attempt to recognize fingerspelling sequences in this challenging setting. Unlike prior work, our video data is extremely challenging due to low frame rates and visual variability. To tackle the visual challenges, we train a special-purpose signing hand detector using a small subset of our data. Given the hand detector output, a sequence model decodes the hypothesized fingerspelled letter sequence. For the sequence model, we explore attention-based recurrent encoder-decoders and CTC-based approaches. As the first attempt at fingerspelling recognition in the wild, this work is intended to serve as a baseline for future work on sign language recognition in realistic conditions. We find that, as expected, letter error rates are much higher than in previous work on more controlled data, and we analyze the sources of error and effects of model variants.Index Terms-American Sign Language, fingerspelling, connectionist temporal classification, attention models 2 Two-handed fingerspelling occasionally occurs, including in our data.

Sign language recognition is a challenging gesture sequence recognition problem, characterized by quick and highly coarticulated motion. In this paper we focus on recognition of fingerspelling sequences in American Sign Language (ASL) videos collected in the wild, mainly from YouTube and Deaf social media. Most previous work on sign language recognition has focused on controlled settings where the data is recorded in a studio environment and the number of signers is limited. Our work aims to address the challenges of real-life data, reducing the need for detection or segmentation modules commonly used in this domain. We propose an end-to-end model based on an iterative attention mechanism, without explicit hand detection or segmentation. Our approach dynamically focuses on increasingly high-resolution regions of interest. It outperforms prior work by a large margin. We also introduce a newly collected data set of crowdsourced annotations of fingerspelling in the wild, and show that performance can be further improved with this additional data set.

Proceedings of the Sixteenth Annual Meeting of the Berkeley Linguistics Society (1990), pp. 46-56