Description
We investigate a method for selecting recordings of human face and head movements from a sign language corpus to serve as a basis for generating animations of novel sentences of American Sign Language (ASL). Drawing from a collection of recordings that have been categorized into various types of non-manual expressions (NMEs), we define a method for selecting an exemplar recording of a given type using a centroid-based selection procedure, using multivariate dynamic time warping (DTW) as the distance function. Through intra- and inter-signer methods of evaluation, we demonstrate the efficacy of this technique, and we note useful potential for the DTW visualizations generated in this study for linguistic researchers collecting and analyzing sign language corpora.
Date of creation, presentation, or exhibit
5-23-2016
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Document Type
Conference Paper
Department, Program, or Center
Information Sciences and Technologies (GCCIS)
Recommended Citation
Kacorri, Hernisa; Syed, Ali Raza; Huenerfauth, Matt; and Neidle, Carol, "Centroid-Based Exemplar Selection of ASL Non-Manual Expressions using Multidimensional Dynamic Time Warping and MPEG4 Features" (2016). Accessed from
https://repository.rit.edu/other/893
Campus
RIT – Main Campus
Comments
Presented at the 7th Workshop on the Representation and Processing of Sign Languages: Corpus Mining, The 10th International Conference on Language Resources and Evaluation (LREC 2016), May 23-28, 2016, Portorož, Slovenia