TY - GEN
T1 - Evaluation of manual and non-manual components for sign language recognition
AU - Mukushev, Medet
AU - Sabyrov, Arman
AU - Imashev, Alfarabi
AU - Koishybay, Kenessary
AU - Kimmelman, Vadim
AU - Sandygulova, Anara
N1 - Funding Information:
This work was supported by the Nazarbayev University Faculty Development Competitive Research Grant Program 2019-2021 “Kazakh Sign Language Automatic Recognition System (K-SLARS)”. Award number is 110119FD4545.
Publisher Copyright:
© European Language Resources Association (ELRA), licensed under CC-BY-NC
PY - 2020
Y1 - 2020
N2 - The motivation behind this work lies in the need to differentiate between similar signs that differ in non-manual components present in any sign. To this end, we recorded full sentences signed by five native signers and extracted 5200 isolated sign samples of twenty frequently used signs in Kazakh-Russian Sign Language (K-RSL), which have similar manual components but differ in non-manual components (i.e. facial expressions, eyebrow height, mouth, and head orientation). We conducted a series of evaluations in order to investigate whether non-manual components would improve sign's recognition accuracy. Among standard machine learning approaches, Logistic Regression produced the best results, 78.2% of accuracy for dataset with 20 signs and 77.9% of accuracy for dataset with 2 classes (statement vs question).
AB - The motivation behind this work lies in the need to differentiate between similar signs that differ in non-manual components present in any sign. To this end, we recorded full sentences signed by five native signers and extracted 5200 isolated sign samples of twenty frequently used signs in Kazakh-Russian Sign Language (K-RSL), which have similar manual components but differ in non-manual components (i.e. facial expressions, eyebrow height, mouth, and head orientation). We conducted a series of evaluations in order to investigate whether non-manual components would improve sign's recognition accuracy. Among standard machine learning approaches, Logistic Regression produced the best results, 78.2% of accuracy for dataset with 20 signs and 77.9% of accuracy for dataset with 2 classes (statement vs question).
KW - Information extraction
KW - Machine learning methods
KW - Sign language Recognition
KW - Statistical
UR - https://www.scopus.com/pages/publications/85095539455
UR - https://www.scopus.com/pages/publications/85095539455#tab=citedBy
M3 - Conference contribution
AN - SCOPUS:85095539455
T3 - LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings
SP - 6073
EP - 6078
BT - LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings
A2 - Calzolari, Nicoletta
A2 - Bechet, Frederic
A2 - Blache, Philippe
A2 - Choukri, Khalid
A2 - Cieri, Christopher
A2 - Declerck, Thierry
A2 - Goggi, Sara
A2 - Isahara, Hitoshi
A2 - Maegaard, Bente
A2 - Mariani, Joseph
A2 - Mazo, Helene
A2 - Moreno, Asuncion
A2 - Odijk, Jan
A2 - Piperidis, Stelios
PB - European Language Resources Association (ELRA)
T2 - 12th International Conference on Language Resources and Evaluation, LREC 2020
Y2 - 11 May 2020 through 16 May 2020
ER -