What is the performance of the best model in the sign language recognition task?
Spatial AI-LSTM
Voice-controlled personal and home assistants (such as the Amazon Echo and Apple Siri) are becoming increasingly popular for a variety of applications. However, the benefits of these technologies are not readily accessible to Deaf or Hard-ofHearing (DHH) users. The objective of this study is to develop and evaluate a sign recognition system using multiple modalities that can be used by DHH signers to interact with voice-controlled devices. With the advancement of depth sensors, skeletal data is used for applications like video analysis and activity recognition. Despite having similarity with the well-studied human activity recognition, the use of 3D skeleton data in sign language recognition is rare. This is because unlike activity recognition, sign language is mostly dependent on hand shape pattern. In this work, we investigate the feasibility of using skeletal and RGB video data for sign language recognition using a combination of different deep learning architectures. We validate our results on a large-scale American Sign Language (ASL) dataset of 12 users and 13107 samples across 51 signs. It is named as GMUASL51. 1 We collected the dataset over 6 months and it will be publicly released in the hope of spurring further machine learning research towards providing improved accessibility for digital assistants.
According to The National Institute on Deafness, one in thousand infants is born deaf. An additional one to six per thousand are born with hearing loss at different levels BIBREF0. Sign language is commonly used by Deaf and Hard-of-Hearing (DHH) persons to communicate via hand gestures. An automatic sign language recognizer enables an ASL user to translate the sign language to written text or speech, allowing them to communicate with people who are not familiar with ASL. There is a tremendous rise in the popularity of personal digital assistants; available on user's personal and wearable devices (Google Now, Amazon Alexa and Apple Siri, etc.) and also in the form of standalone devices (Amazon Echo and Google Home smart speakers). These devices are primarily controlled through voice, and hence, their functionality is not readily available to DHH users. An automatic sign recognizer can also enable the interaction between a DHH user and a digital assistant. Most current systems have capability of ASL recognition with RGB video data BIBREF1, BIBREF2, BIBREF3. An ASL sign is performed by a combination of hand gestures, facial expressions and postures of the body. Sequential motion of specific body locations (such as hand-tip, neck and arm) provide informative cues about a sign. Using video data, it is difficult to extract different body locations and associated motion sequences from a series of RGB frames. Microsoft Kinect is a 3D camera sensor which can use the depth information of a person to capture 3D coordinates of his/her body location across a video. This sequence of 3D body location is referred by skeletal data BIBREF4. To the best of our knowledge, there is no publicly available skeletal dataset in literature for ASL recognition. With skeletal data, an ASL sign can be seen as a sequence of 3D coordinates or a 3D time series BIBREF5. Recurrent neural networks (RNN) have shown strong performance for sequential modeling BIBREF6. In this work, we investigate the impact of RGB video data in recognition accuracy when combined with skeletal data. We also propose a combined RNN network with a simple spatial data augmentation technique. In summary, the contributions of this work are: We propose an RNN architecture with a novel spatial data augmentation technique. We propose an architecture which uses both RGB and skeletal data to improve recognition accuracy. We introduce and publicly release a multi–modal dataset for ASL called GMU-ASL51.