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1. The unbounded productivity of (sign) language: Evidence from the Stroop task

   https://doi.org/10.1075/ml.17016.ber  

   Berent, Iris ; Dupuis, Amanda ( March 2018 , The Mental Lexicon)

   Unbounded productivity is a hallmark of linguistic competence. Here, we asked whether this capacity automatically applies to signs. Participants saw video-clips of novel signs in American Sign Language (ASL) produced by a signer whose body appeared in a monochromatic color, and they quickly identified the signs’ color. The critical manipulation compared reduplicative (αα) signs to non-reduplicative (αβ) controls. Past research has shown that reduplication is frequent in ASL, and frequent structures elicit stronger Stroop interference. If signers automatically generalize the reduplication function, then αα signs should elicit stronger color-naming interference. Results showed no effect of reduplication for signs whose basemore »(α) consisted of native ASL features (possibly, due to the similarity of α items to color names). Remarkably, signers were highly sensitive to reduplication when the base (α) included novel features. These results demonstrate that signers can freely extend their linguistic knowledge to novel forms, and they do so automatically. Unbounded productivity thus defines all languages, irrespective of input modality.« less
2. Isolated Sign Recognition using ASL Datasets with Consistent Text-based Gloss Labeling and Curriculum Learning

   Dafnis, Konstantinos M. ; Chroni, Evgenia ; Neidle, Carol ; Metaxas, Dimitris ( June 2022 , Seventh International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual (SLTAT 2022). LREC, Marseille, France, June 2022)

   We present a new approach for isolated sign recognition, which combines a spatial-temporal Graph Convolution Network (GCN) architecture for modeling human skeleton keypoints with late fusion of both the forward and backward video streams, and we explore the use of curriculum learning. We employ a type of curriculum learning that dynamically estimates, during training, the order of difficulty of each input video for sign recognition; this involves learning a new family of data parameters that are dynamically updated during training. The research makes use of a large combined video dataset for American Sign Language (ASL), including data from both themore »American Sign Language Lexicon Video Dataset (ASLLVD) and the Word-Level American Sign Language (WLASL) dataset, with modified gloss labeling of the latter—to ensure 1-1 correspondence between gloss labels and distinct sign productions, as well as consistency in gloss labeling across the two datasets. This is the first time that these two datasets have been used in combination for isolated sign recognition research. We also compare the sign recognition performance on several different subsets of the combined dataset, varying in, e.g., the minimum number of samples per sign (and therefore also in the total number of sign classes and video examples).« less
3. Dynamic Cross-Feature Fusion for American Sign Language Translation

   https://doi.org/10.1109/FG52635.2021.9667027  

   Ananthanarayana, Tejaswini ; Kotecha, Nikunj ; Srivastava, Priyanshu ; Chaudhary, Lipisha ; Wilkins, Nicholas ; Nwogu, Ifeoma ( December 2021 , 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021))

   While a significant amount of work has been done on the commonly used, tightly -constrained weather-based, German sign language (GSL) dataset, little has been done for continuous sign language translation (SLT) in more realistic settings, including American sign language (ASL) translation. Also, while CNN - based features have been consistently shown to work well on the GSL dataset, it is not clear whether such features will work as well in more realistic settings when there are more heterogeneous signers in non-uniform backgrounds. To this end, in this work, we introduce a new, realistic phrase-level ASL dataset (ASLing), and explore themore »role of different types of visual features (CNN embeddings, human body keypoints, and optical flow vectors) in translating it to spoken American English. We propose a novel Transformer-based, visual feature learning method for ASL translation. We demonstrate the explainability efficacy of our proposed learning methods by visualizing activation weights under various input conditions and discover that the body keypoints are consistently the most reliable set of input features. Using our model, we successfully transfer-learn from the larger GSL dataset to ASLing, resulting in significant BLEU score improvements. In summary, this work goes a long way in bringing together the AI resources required for automated ASL translation in unconstrained environments.« less
4. Scalable ASL Sign Recognition using Model-based Machine Learning and Linguistically Annotated Corpora

   Metaxas, Dimitris ; Dilsizian, Mark ; Neidle, Carol ( May 2018 , 8th Workshop on the Representation & Processing of Sign Languages: Involving the Language Community, Language Resources and Evaluation Conference 2018)

   We report on the high success rates of our new, scalable, computational approach for sign recognition from monocular video, exploiting linguistically annotated ASL datasets with multiple signers. We recognize signs using a hybrid framework combining state-of-the-art learning methods with features based on what is known about the linguistic composition of lexical signs. We model and recognize the sub-components of sign production, with attention to hand shape, orientation, location, motion trajectories, plus non-manual features, and we combine these within a CRF framework. The effect is to make the sign recognition problem robust, scalable, and feasible with relatively smaller datasets than aremore »required for purely data-driven methods. From a 350-sign vocabulary of isolated, citation-form lexical signs from the American Sign Language Lexicon Video Dataset (ASLLVD), including both 1- and 2-handed signs, we achieve a top-1 accuracy of 93.3% and a top-5 accuracy of 97.9%. The high probability with which we can produce 5 sign candidates that contain the correct result opens the door to potential applications, as it is reasonable to provide a sign lookup functionality that offers the user 5 possible signs, in decreasing order of likelihood, with the user then asked to select the desired sign.« less
5. Scalable ASL Sign Recognition using Model-based Machine Learning and Linguistically Annotated Corpora.

   Metaxas, Dimitris ; Dilsizian, Mark ; Neidle, Carol ( April 2018 , 8th Workshop on the Representation & Processing of Sign Languages: Involving the Language Community, Language Resources and Evaluation Conference)

   We report on the high success rates of our new, scalable, computational approach for sign recognition from monocular video, exploiting linguistically annotated ASL datasets with multiple signers. We recognize signs using a hybrid framework combining state-of-the-art learning methods with features based on what is known about the linguistic composition of lexical signs. We model and recognize the sub-components of sign production, with attention to hand shape, orientation, location, motion trajectories, plus non-manual features, and we combine these within a CRF framework. The effect is to make the sign recognition problem robust, scalable, and feasible with relatively smaller datasets than aremore »required for purely data-driven methods. From a 350-sign vocabulary of isolated, citation-form lexical signs from the American Sign Language Lexicon Video Dataset (ASLLVD), including both 1- and 2-handed signs, we achieve a top-1 accuracy of 93.3% and a top-5 accuracy of 97.9%. The high probability with which we can produce 5 sign candidates that contain the correct result opens the door to potential applications, as it is reasonable to provide a sign lookup functionality that offers the user 5 possible signs, in decreasing order of likelihood, with the user then asked to select the desired sign.« less