5.3 The VisSE project

During the authors’ research in Spanish Sign Language, the problems outlined in the introduction regarding its digital treatment were patent. As students of this language as well as researchers and engineers, ideas for solutions started to come to our minds. At some point, previous expertise in image recognition, a very salient topic in sign language research, joined the knowledge of SignWriting as a useful tool for these languages, used by our educators and many in the Deaf community.

Some of the ideas for both tools and processes were combined into a single effort for which funding was requested, and granted by Indra and Fundación Universia as a grant for research on Accessible Technologies. This effort resulted in the VisSE project (“Visualizando la SignoEscritura”, Spanish for “Visualizing SignWriting”) aimed at developing tools for the effective use of SignWriting in computers. These tools can help with the integration of Hard of Hearing people in the digital society, and will also help accelerate sign language research by providing another methodology for its research.

A general architecture of the project can be seen in Figure 5.2. There, the sign in Spanish Sign Language for “teacher” is used as an example. Its transcription in SignWriting is decomposed and processed by an artificial vision algorithm, which finds the different symbols and classifies them. The labels and relative positions of the symbols are then transformed into their linguistic meaning, called here “parametrization”. In the example, the usual features of sign language analysis are used, but this representation is yet to be decided, and has to follow closely the information encoded in the SignWriting transcription. The parametrization is then turned into a textual description, which allows a non-signer to realize the sign, and a 3D animation which can be understood by a signer.

Architecture of the different components of the VisSE project.
Fig. 5.2 − Architecture of the different components of the VisSE project.

5.3.1 Corpus of SignWriting Transcriptions

While the goal of the project is to develop the tools mentioned before, which will help with the use of SignWriting in the digital world, there will be an additional language resource result. Data are of paramount importance when doing computational linguistics, and the artificial vision algorithms to be used rely on these data for their successful training and use.

Therefore, one of the products of the project will be a corpus of linguistic annotations. Entries in the corpus will be, as far as possible, input by informants who are native signers of Spanish Sign Language. For this purpose, a custom computer interface will be developed. This interface needs only be a simple front-end to the database, with roles for informants and for corpus managers, and with some tool to facilitate SignWriting input, either by a point-and-click interface or by a hand-drawing or scanner technology. Annotation, however, will not consist of grammatical information, but rather of the locations and meanings of the different symbols in the transcription.

Even if less interesting to our users, this result will probably be of use to other researchers, so it too will be publicly released. Similar to other such projects, the main object of annotation will be lexical entries, words of sign language and their realization, the main difference being that the data recorded will be in the form of SignWriting. The meaning of the annotated sign will be transcribed using an appropriate translation in Spanish.

Corpora that peruse SignWriting already exist (Forster et al. 2014), and there is also SignBank1, a collection of tools and resources related to SignWriting, including dictionaries for many sign languages around the world, and SignMaker, an interface for the creation of SignWriting images. While useful, the data available in the dictionaries are limited, especially for languages other than American Sign Language, and its interface is more oriented toward small-scale, manual research rather than large-scale, automated computation.

5.3.2 Transcription Recognizer

At first, the annotations in the corpus will have to be performed by humans, but they will immediately serve as training data for the YOLO algorithm explained in Section 5.2.2. As annotation advances, so will increase the performance of the automatic recognition, which will be used to help annotators in their process by providing them with the prediction from the algorithm as a draft. This will accelerate data collection, which will in turn increase training effectiveness until at some point the algorithm will be able to recognize most input on its own.

The use of YOLO for recognition of SignWriting has already been successfully prototyped by students of ours (Sánchez Jiménez, López Prieto, y Garrido Montoya 2019). The located and classified symbols found by the algorithm will then be transformed into the representation used in the corpus, which will include the linguistically relevant parameters (for example, it is relevant that the location is “at head level”, but not whether the transcription is drawn 7 pixels to the right).

This process of finding out sign language parameters using computer vision is akin to that of automatic sign language recognition in video, which is often performed for video-based corpora. However, it is much simpler, both for the human annotator and the computer vision algorithm, since images are black and white, standardized and far less noisy.

Transcriptions, being composed out of a discrete (even if large) amount of symbols, present an additional advantage: training data can be immensely augmented by automatic means. An algorithm, already implemented, mixes the possible symbols to create random images which contain the data and annotations the YOLO algorithm needs to be trained. An example of this can be seen in Figure 5.3. Even if these are meaningless as SignWriting transcriptions, they contain the features and patterns that the algorithm needs to learn. This will help bootstrap the system, which will make the recognizer useful to annotators earlier rather than later during the project.

Fig. 5.3 − Automatically generated training samples for YOLO.

5.3.3 Description of the Sign

Once the algorithm is able to understand transcriptions, its parametric result will be used in tools that can help integrate the Deaf community in Spain into the digital world.

The first such tool will be a generator of textual descriptions from SignWriting parametrizations. This description will explain, in a language easy to understand, the articulation and movements codified in the SignWriting symbols.

This will be a useful aid in communication between signers and non-signers. For example, it can be explained to non-Deaf people how to sign basic vocabulary, like pleasantries, or maybe important words in a particular domain (an office, a factory). This will allow non-Deaf people communicate to Deaf people basic information, useful for the daily routine or maybe an emergency, without the need to learn sign language proper. This may help broaden the employability landscape for Deaf people, increasing their inclusion in the office community and with their non-signing peers.

The use of text instead of video has some advantages. While observation of real video and images is necessary for proper understanding of the rhythm and cadence of sign language, it is often not enough for correct articulation and orientation of the hand. Non-signers are not used to looking for the visual cues in hand articulation, and may confuse the hand configurations necessary for particular signs. Spelling them out, however, can help them realize the correct finger flexing and wrist rotation, in an environment where an interpreter or teacher may not be readily available.

This also leads to a different application of this tool: education. Both Deaf and non-Deaf people can find it challenging to self-study sign language, due to the scarcity of resources and the challenge of a lack of a linear transcription system. Translating SignWriting into text can improve understanding of this system, helping both signers and students learn the representation of sign language in SignWriting symbols in a dynamic environment with immediate feedback.

5.3.4 Animated Execution by an Avatar

Another result of the project will be a three-dimensional animated avatar, capable of executing the signs contained in the parametric representation. SignWriting is an (almost) complete transcription of sign language, including spatial and movement-related information. This information, after its computational transformation into parameters, can be directly fed into a virtual avatar to realize the signs in three dimensions.

The advantages of the use of avatars are known in the community, and have been studied before. Kipp, Heloir, y Nguyen (2011) give an informative account of different avatar technologies and the challenges in their use, and propose methodologies for testing and evaluation of the results. Bouzid y Jemni (2014) describe an approach very similar to ours in its goal, using SignWriting as the basis for generating the sign language animations. However, this process is not done automatically, but manually as in other avatar technologies.

Manual preparation of the execution of signs is a costly procedure, even if not as much as video-taping interpreters. An expert in the system as well as one in sign language are needed, and it is difficult to find both in one person. With our approach, instead of knowing the intricacies of the avatar technology, just an expert in SignWriting would be needed. It is far easier that this expert is the same as the sign language translator or author, or minimal training can be provided. SignWriting is also easier to carry around and edit, compared to systems like SIGML (Kennaway 2004), which may be intuitive and easy for computer engineers but no so much to non-computer-savvy users.

Our system will also strive to be dynamic, not presenting a static sequence of images but rather an actual animation of the sign. While sign language generation is very complicated, it is important to note that this is not what our system needs to do. Placement and movement are already encoded in SignWriting, and our system only needs to convert the parameters into actual coordinates in three-dimensional space.

The technology for this tool will be Javascript and WebGL, which are increasingly mature and seeing wide-spread adoption in the industry. Web technology is ubiquitous nowadays, and browsers present an ideal execution environment where users need not install specific libraries or software but rather use the same program they use in their everyday digital lives.