-
Journal of Imaging2023Several sign language datasets are available in the literature. Most of them are designed for sign language recognition and translation. This paper presents a new sign language dataset for automatic motion generation. This dataset includes phonemes for each sign (specified in HamNoSys, a transcription system developed at the University of Hamburg, Hamburg, Germany) and the corresponding motion information
-
MDPI Sensors Journal2023This paper proposes, analyzes, and evaluates a deep learning architecture based on transformers for generating sign language motion from sign phonemes (represented using HamNoSys: a notation system developed at the University of Hamburg). The sign phonemes provide information about sign characteristics like hand configuration, localization, or movements. The use of sign phonemes is crucial for generating
-
NeurIPS 2023 Workshop on Robustness of Zero/Few-shot Learning in Foundation Models (R0-FoMo)2023With the recent surge of language models in different applications, attention to safety and robustness of these models has gained significant importance. Here we introduce a joint framework in which we simultaneously probe and improve the robustness of a black-box target model via adversarial prompting and belief augmentation using iterative feedback loops. This framework utilizes an automated red teaming
-
NeurIPS 2023 Workshop on Efficient Natural Language and Speech Processing (ENLSP-III)2023While data selection methods have been studied extensively in active learning, data pruning, and data augmentation settings, there is little evidence for the efficacy of these methods in industry scale settings, particularly in low-resource languages. Our work presents ways of assessing prospective training examples in those settings for their "usefulness" or "difficulty". We also demonstrate how these
-
NeurIPS 2023 Workshop on I Can’t Believe It’s Not Better (ICBINB): Failure Modes in the Age of Foundation Models2023Numerous Natural Language Processing (NLP) tasks require precisely labeled data to ensure effective model training and achieve optimal performance. However, data annotation is marked by substantial costs and time requirements, especially when requiring specialized domain expertise or annotating a large number of samples. In this study, we investigate the feasibility of employing large language models (LLMs
Related content
-
July 03, 2023With little training data and no mapping of speech to phonemes, Amazon researchers used voice conversion to generate Irish-accented training data in Alexa’s own voice. -
June 26, 2023How phonetically blended results (PBR) help ensure customers find the content they were actually asking for.
-
June 09, 2023In a top-3% paper at ICASSP, Amazon researchers adapt graph-based label propagation to improve speech recognition on underrepresented pronunciations.
-
June 07, 2023Team earned $500,000 for its performance in a challenge focused on advancing next-generation virtual assistants that help humans complete real-world tasks by continuously learning.
-
June 07, 2023Combining semi-supervised learning, data augmentation, and reinforcement learning using rewards based on implicit customer feedback and natural-language-understanding semantics reduces word error rate by more than 10%.
-
June 02, 2023Topics such as code generation, commonsense reasoning, and self-learning complement the usual focus on speech recognition and acoustic-event classification.
