Papers with word recognition
The neural dynamics of word recognition and integration (2023.emnlp-main)
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| Challenge: | Using a computational model of word recognition, listeners combine expectations about upcoming content with incremental sensory evidence. |
| Approach: | They fit this model to scalp EEG signals recorded as subjects passively listened to a fictional story and found that words require more than 150 ms of input to be recognized. |
| Outcome: | The proposed model formalizes this perceptual process in Bayesian decision theory and reveals distinct neural processing of words depending on whether or not they can be quickly recognized. |
Speakers enhance contextually confusable words (2020.acl-main)
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| Challenge: | Recent work has found that natural languages are shaped by pressures for efficient communication. |
| Approach: | They develop a measure of contextual confusability during word recognition based on psychoacoustic data and apply it to naturalistic speech corpora. |
| Outcome: | The proposed measure of confusability suggests that speakers alter productions to make contextually more confused words easier to understand. |
Combating Adversarial Misspellings with Robust Word Recognition (P19-1)
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| Challenge: | a sub-field of word recognition models is emerging to combat adversarial spelling mistakes . imperceptible attacks can cause models to misclassify examples, but training robust models remains a challenge . |
| Approach: | They propose to place a word recognition model in front of a downstream classifier to combat adversarial spelling mistakes. |
| Outcome: | The proposed model outperforms adversarial training and off-the-shelf spell checkers in a word recognition task. |
CISLR: Corpus for Indian Sign Language Recognition (2022.emnlp-main)
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Abhinav Joshi, Ashwani Bhat, Pradeep S, Priya Gole, Shashwat Gupta, Shreyansh Agarwal, Ashutosh Modi
| Challenge: | Existing work on natural language processing has shown promising improvements in text classification, translation and generation in widely used spoken languages. |
| Approach: | They propose a new Indian Sign Language corpus for word-level recognition using videos . they propose CISLR model that leverages resource rich American Sign Language to learn generalized features for improving Indian Sign language predictions. |
| Outcome: | The proposed model improves word recognition in Indian Sign Language using video . it leverages resource rich American Sign Language to learn generalized features . |
Emergent morpho-phonological representations in self-supervised speech models (2025.emnlp-main)
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| Challenge: | a recent study shows that self-supervised speech models do not represent phonological and morphological phenomena in frequent English noun and verb inflections. |
| Approach: | They study how S3Ms represent phonological and morphological phenomena in English . they propose alternative representational strategies that may support human spoken word recognition . |
| Outcome: | a new study shows that S3M models can represent phonological and morphological phenomena in English . the models can be trained to recognize spoken words in naturalistic, noisy environments . |