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Sign Language Recognition (shortened generally as SLR) is a computational task that involves recognizing actions from sign languages. [1] This is an essential problem to solve especially in the digital world to bridge the communication gap that is faced by people with hearing impairments.
Whisper is a machine learning model for speech recognition and transcription, created by OpenAI and first released as open-source software in September 2022. [2]It is capable of transcribing speech in English and several other languages, and is also capable of translating several non-English languages into English. [1]
Sign language translation technologies are limited in the same way as spoken language translation. None can translate with 100% accuracy. In fact, sign language translation technologies are far behind their spoken language counterparts. This is, in no trivial way, due to the fact that signed languages have multiple articulators.
For example, in sign language, each gesture represents a word or phrase. Some literature differentiates 2 different approaches in gesture recognition: a 3D model-based and an appearance-based. [ 31 ] The foremost method makes use of 3D information on key elements of the body parts in order to obtain several important parameters, like palm ...
The use of speech recognition is more naturally suited to the generation of narrative text, as part of a radiology/pathology interpretation, progress note or discharge summary: the ergonomic gains of using speech recognition to enter structured discrete data (e.g., numeric values or codes from a list or a controlled vocabulary) are relatively ...
A sign language glove is an electronic device which attempts to convert the motions of a sign language into written or spoken words. Some critics of such technologies have argued that the potential of sensor-enabled gloves to do this is commonly overstated or misunderstood, because many sign languages have a complex grammar that includes use of the sign space and facial expressions (non-manual ...
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Various (filtering, language classification, adult-content detection and other labelling) 3.4 TB English text, 1.4 TB Chinese text, 1.1 TB Russian text, 595 MB German text, 431 MB French text, and data for 150+ languages (figures for version 23.01)