Best practices for conducting evaluations of sign language animation

Automatic synthesis of linguistically accurate and natural-looking American Sign Language (ASL) animations would make it easier to add ASL content to websites and media, thereby increasing information accessibility for many people who are deaf. Based on several years of studies, we identify best practices for conducting experimental evaluations of sign language animations with feedback from deaf and hard-of-hearing users. First, we describe our techniques for identifying and screening participants, and for controlling the experimental environment. Finally, we discuss rigorous methodological research on how experiment design affects study outcomes when evaluating sign language animations. Our discussion focuses on stimuli design, effect of using videos as an upper baseline, using videos for presenting comprehension questions, and eye-tracking as an alternative to recording question-responses

Computer-based tracking, analysis, and visualization of linguistically significant nonmanual events in American Sign Language (ASL)

Our linguistically annotated American Sign Language (ASL) corpora have formed a basis for research to automate detection by computer of essential linguistic information conveyed through facial expressions and head movements. We have tracked head position and facial deformations, and used computational learning to discern specific grammatical markings. Our ability to detect, identify, and temporally localize the occurrence of such markings in ASL videos has recently been improved by incorporation of (1) new techniques for deformable model-based 3D tracking of head position and facial expressions, which provide significantly better tracking accuracy and recover quickly from temporary loss of track due to occlusion; and (2) a computational learning approach incorporating 2-level Conditional Random Fields (CRFs), suited to the multi-scale spatio-temporal characteristics of the data, which analyses not only low-level appearance characteristics, but also the patterns that enable identification of significant gestural components, such as periodic head movements and raised or lowered eyebrows. Here we summarize our linguistically motivated computational approach and the results for detection and recognition of nonmanual grammatical markings; demonstrate our data visualizations, and discuss the relevance for linguistic research; and describe work underway to enable such visualizations to be produced over large corpora and shared publicly on the Web

Eyetracking Metrics Related to Subjective Assessments of ASL Animations

Analysis of eyetracking data can serve as an alternative method of evaluation when assessing the quality of computer-synthesized animations of American Sign Language (ASL), technology which can make information accessible to people who are deaf or hard-of-hearing, who may have lower levels of written language literacy. In this work, we build and evaluate the efficacy of descriptive models of subjective scores that native signers assign to ASL animations, based on eye-tracking metrics

Evaluating American Sign Language Generation Through the Participation of Native ASL Signers

We discuss important factors in the design of evaluation studies for systems that generate animations of American Sign Language (ASL) sentences. In particular, we outline how some cultural and linguistic characteristics of members of the American Deaf community must be taken into account so as to ensure the accuracy of evaluations involving these users. Finally, we describe our implementation and user-based evaluation (by native ASL signers) of a prototype ASL generator to produce sentences containing classifier predicates, frequent and complex spatial phenomena that previous ASL generators have not produced

Selecting Exemplar Recordings of American Sign Language Non-Manual Expressions for Animation Synthesis Based on Manual Sign Timing

Animations of sign language can increase the accessibility of information for people who are deaf or hard of hearing (DHH), but prior work has demonstrated that accurate non-manual expressions (NMEs), consisting of face and head movements, are necessary to produce linguistically accurate animations that are easy to understand. When synthesizing animation, given a sequence of signs performed on the hands (and their timing), we must select an NME performance. Given a corpus of facial motion-capture recordings of ASL sentences with annotation of the timing of signs in the recording, we investigate methods (based on word count and on delexicalized sign timing) for selecting the best NME recoding to use as a basis for synthesizing a novel animation. By comparing recordings selected using these methods to a gold-standard recording, we identify the top-performing exemplar selection method for several NME categories

TR-2015001: A Survey and Critique of Facial Expression Synthesis in Sign Language Animation

Sign language animations can lead to better accessibility of information and services for people who are deaf and have low literacy skills in spoken/written languages. Due to the distinct word-order, syntax, and lexicon of the sign language from the spoken/written language, many deaf people find it difficult to comprehend the text on a computer screen or captions on a television. Animated characters performing sign language in a comprehensible way could make this information accessible. Facial expressions and other non-manual components play an important role in the naturalness and understandability of these animations. Their coordination to the manual signs is crucial for the interpretation of the signed message. Software to advance the support of facial expressions in generation of sign language animation could make this technology more acceptable for deaf people. In this survey, we discuss the challenges in facial expression synthesis and we compare and critique the state of the art projects on generating facial expressions in sign language animations. Beginning with an overview of facial expressions linguistics, sign language animation technologies, and some background on animating facial expressions, a discussion of the search strategy and criteria used to select the five projects that are the primary focus of this survey follows. This survey continues on to introduce the work from the five projects under consideration. Their contributions are compared in terms of support for specific sign language, categories of facial expressions investigated, focus range in the animation generation, use of annotated corpora, input data or hypothesis for their approach, and other factors. Strengths and drawbacks of individual projects are identified in the perspectives above. This survey concludes with our current research focus in this area and future prospects

Modeling the Speed and Timing of American Sign Language to Generate Realistic Animations

While there are many Deaf or Hard of Hearing (DHH) individuals with excellent reading literacy, there are also some DHH individuals who have lower English literacy. American Sign Language (ASL) is not simply a method of representing English sentences. It is possible for an individual to be fluent in ASL, while having limited fluency in English. To overcome this barrier, we aim to make it easier to generate ASL animations for websites, through the use of motion-capture data recorded from human signers to build different predictive models for ASL animations; our goal is to automate this aspect of animation synthesis to create realistic animations. This dissertation consists of several parts: Part I, defines key terminology for timing and speed parameters, and surveys literature on prior linguistic and computational research on ASL. Next, the motion-capture data that our lab recorded from human signers is discussed, and details are provided about how we enhanced this corpus to make it useful for speed and timing research. Finally, we present the process of adding layers of linguistic annotation and processing this data for speed and timing research. Part II presents our research on data-driven predictive models for various speed and timing parameters of ASL animations. The focus is on predicting the (1) existence of pauses after each ASL sign, (2) predicting the time duration of these pauses, and (3) predicting the change of speed for each ASL sign within a sentence. We measure the quality of the proposed models by comparing our models with state-of-the-art rule-based models. Furthermore, using these models, we synthesized ASL animation stimuli and conducted a user-based evaluation with DHH individuals to measure the usability of the resulting animation. Finally, Part III presents research on whether the timing parameters individuals prefer for animation may differ from those in recordings of human signers. Furthermore, it also includes research to investigate the distribution of acceleration curves in recordings of human signers and whether utilizing a similar set of curves in ASL animations leads to measurable improvements in DHH users\u27 perception of animation quality

Data-Driven Synthesis and Evaluation of Syntactic Facial Expressions in American Sign Language Animation

Technology to automatically synthesize linguistically accurate and natural-looking animations of American Sign Language (ASL) would make it easier to add ASL content to websites and media, thereby increasing information accessibility for many people who are deaf and have low English literacy skills. State-of-art sign language animation tools focus mostly on accuracy of manual signs rather than on the facial expressions. We are investigating the synthesis of syntactic ASL facial expressions, which are grammatically required and essential to the meaning of sentences. In this thesis, we propose to: (1) explore the methodological aspects of evaluating sign language animations with facial expressions, and (2) examine data-driven modeling of facial expressions from multiple recordings of ASL signers. In Part I of this thesis, we propose to conduct rigorous methodological research on how experiment design affects study outcomes when evaluating sign language animations with facial expressions. Our research questions involve: (i) stimuli design, (ii) effect of videos as upper baseline and for presenting comprehension questions, and (iii) eye-tracking as an alternative to recording question-responses from participants. In Part II of this thesis, we propose to use generative models to automatically uncover the underlying trace of ASL syntactic facial expressions from multiple recordings of ASL signers, and apply these facial expressions to manual signs in novel animated sentences. We hypothesize that an annotated sign language corpus, including both the manual and non-manual signs, can be used to model and generate linguistically meaningful facial expressions, if it is combined with facial feature extraction techniques, statistical machine learning, and an animation platform with detailed facial parameterization. To further improve sign language animation technology, we will assess the quality of the animation generated by our approach with ASL signers through the rigorous evaluation methodologies described in Part I