Advances in phonetics-based sub-unit modeling for transcription alignment and sign language recognition
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Citations
Towards Multilingual Sign Language Recognition
American Sign Language fingerspelling recognition from video: Methods for unrestricted recognition and signer-independence.
Independent Sign Language Recognition with 3d Body, Hands, and Face Reconstruction
A Computational Study of American Sign Language Nonmanuals
Korean Sign Language Recognition Using Transformer-Based Deep Neural Network
References
American Sign Language: The Phonological Base
A Framework for Recognizing the Simultaneous Aspects of American Sign Language
A Linguistic Feature Vector for the Visual Interpretation of Sign Language
Towards an automatic sign language recognition system using subunits
Modelling and segmenting subunits for sign language recognition based on hand motion analysis
Related Papers (5)
Automatic Sign Language Analysis: A Survey and the Future beyond Lexical Meaning
Frequently Asked Questions (8)
Q2. What are the causes of outliers and high variances?
Outliers and high variances seem to be caused by visual processing inaccuracies (we perform 2D, rather than 3D, processing), tracking or parameter estimation errors, or human annotator errors, or actual data exhibiting such properties.
Q3. What are the steps involved in the conversion of the phonetic labels?
The procedures involved in this process involve: (1) phonetic sub-unit construction and training, (2) phonetic label alignment and segmentation, (3) lexicon construction, and (4) recognition.
Q4. how can other disciplines benefit from their results for the analysis of sign languages?
the authors expect that other disciplines, such as linguistics, can greatly benefit from their results for the analysis of sign languages.
Q5. What is the morphological processing used for the detection of the signer’s hands and?
For the segmentation and detection of the signer’s hands and head in the Greek Sign Language (GSL) Lemmas Corpus, the authors employed a skin color model utilizing a Gaussian Markov Model (GMM), accompanied by morphological processing to enhance skin detection.
Q6. What is the meaning of the annotations?
The annotations of the signs are coded in HamNoSys [9], a symbolic annotation system that can describe a sign in sufficient detail to display it in an animated avatar.
Q7. What is the expected effect of increasing the number of signs?
By increasing the number of signs, the recognition performance for both approaches decreases; this is expected as the recognition task becomes harder.
Q8. What is the conversion method for PILE?
Their conversion method from HamNoSys to the PDTS structure resolves the implied parts, and splits the signs into its constituent segments.