Zobrazeno 1 - 10
of 110
pro vyhledávání: '"Huenerfauth, Matt"'
Autor:
Hassan, Saad, Seita, Matthew, Berke, Larwan, Tian, Yingli, Gale, Elaine, Lee, Sooyeon, Huenerfauth, Matt
We are releasing a dataset containing videos of both fluent and non-fluent signers using American Sign Language (ASL), which were collected using a Kinect v2 sensor. This dataset was collected as a part of a project to develop and evaluate computer v
Externí odkaz:
http://arxiv.org/abs/2207.04021
Deaf and hard of hearing individuals regularly rely on captioning while watching live TV. Live TV captioning is evaluated by regulatory agencies using various caption evaluation metrics. However, caption evaluation metrics are often not informed by p
Externí odkaz:
http://arxiv.org/abs/2206.12368
Much of the world's population experiences some form of disability during their lifetime. Caution must be exercised while designing natural language processing (NLP) systems to prevent systems from inadvertently perpetuating ableist bias against peop
Externí odkaz:
http://arxiv.org/abs/2110.00521
As part of the development of an educational tool that can help students achieve fluency in American Sign Language (ASL) through independent and interactive practice with immediate feedback, this paper introduces a near real-time system to recognize
Externí odkaz:
http://arxiv.org/abs/2005.00253
Autor:
Kafle, Sushant, Glasser, Abraham, Al-khazraji, Sedeeq, Berke, Larwan, Seita, Matthew, Huenerfauth, Matt
We discuss issues of Artificial Intelligence (AI) fairness for people with disabilities, with examples drawn from our research on human-computer interaction (HCI) for AI-based systems for people who are Deaf or Hard of Hearing (DHH). In particular, w
Externí odkaz:
http://arxiv.org/abs/1908.10414
Autor:
Bragg, Danielle, Koller, Oscar, Bellard, Mary, Berke, Larwan, Boudrealt, Patrick, Braffort, Annelies, Caselli, Naomi, Huenerfauth, Matt, Kacorri, Hernisa, Verhoef, Tessa, Vogler, Christian, Morris, Meredith Ringel
Developing successful sign language recognition, generation, and translation systems requires expertise in a wide range of fields, including computer vision, computer graphics, natural language processing, human-computer interaction, linguistics, and
Externí odkaz:
http://arxiv.org/abs/1908.08597
In this paper, we propose a 3D Convolutional Neural Network (3DCNN) based multi-stream framework to recognize American Sign Language (ASL) manual signs (consisting of movements of the hands, as well as non-manual face movements in some cases) in real
Externí odkaz:
http://arxiv.org/abs/1906.02851
Publikováno v:
Proceedings of the Eighth Workshop on Speech and Language Processing for Assistive Technologies. 2019
Prosodic cues in conversational speech aid listeners in discerning a message. We investigate whether acoustic cues in spoken dialogue can be used to identify the importance of individual words to the meaning of a conversation turn. Individuals who ar
Externí odkaz:
http://arxiv.org/abs/1903.12238
Autor:
Kafle, Sushant, Huenerfauth, Matt
Publikováno v:
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)
Motivated by a project to create a system for people who are deaf or hard-of-hearing that would use automatic speech recognition (ASR) to produce real-time text captions of spoken English during in-person meetings with hearing individuals, we have au
Externí odkaz:
http://arxiv.org/abs/1801.09746
Autor:
Kafle, Sushant, Huenerfauth, Matt
Publikováno v:
ASSETS'17 (2017) 165-174
The accuracy of Automated Speech Recognition (ASR) technology has improved, but it is still imperfect in many settings. Researchers who evaluate ASR performance often focus on improving the Word Error Rate (WER) metric, but WER has been found to have
Externí odkaz:
http://arxiv.org/abs/1712.02033