Raja Kushalnagar

Phoebe Chua, Cathy Mengying Fang, Takehiko Ohkawa et al.

Unlike spoken languages where the use of prosodic features to convey emotion is well studied, indicators of emotion in sign language remain poorly understood, creating communication barriers in critical settings. Sign languages present unique challenges as facial expressions and hand movements simultaneously serve both grammatical and emotional functions. To address this gap, we introduce EmoSign, the first sign video dataset containing sentiment and emotion labels for 200 American Sign Language (ASL) videos. We also collect open-ended descriptions of emotion cues. Annotations were done by 3 Deaf ASL signers with professional interpretation experience. Alongside the annotations, we include baseline models for sentiment and emotion classification. This dataset not only addresses a critical gap in existing sign language research but also establishes a new benchmark for understanding model capabilities in multimodal emotion recognition for sign languages. The dataset is made available at https://huggingface.co/datasets/catfang/emosign.

Paige S. DeVries, Michaela Okosi, Ming Li et al.

We investigate intelligent personal assistants (IPAs) accessibility for deaf and hard of hearing (DHH) people who can use their voice in everyday communication. The inability of IPAs to understand diverse accents including deaf speech renders them largely inaccessible to non-signing and speaking DHH individuals. Using an Echo Show, we compare the usability of natural language input via spoken English; with Alexa's automatic speech recognition and a Wizard-of-Oz setting with a trained facilitator re-speaking commands against that of a large language model (LLM)-assisted touch interface in a mixed-methods study. The touch method was navigated through an LLM-powered "task prompter," which integrated the user's history and smart environment to suggest contextually-appropriate commands. Quantitative results showed no significant differences across both spoken English conditions vs LLM-assisted touch. Qualitative results showed variability in opinions on the usability of each method. Ultimately, it will be necessary to have robust deaf-accented speech recognized natively by IPAs.

Colin Lea, Vasileios Baltatzis, Connor Gillis et al.

AI-driven sign language interpretation is limited by a lack of high-quality annotated data. New datasets including ASL STEM Wiki and FLEURS-ASL contain professional interpreters and 100s of hours of data but remain only partially annotated and thus underutilized, in part due to the prohibitive costs of annotating at this scale. In this work, we develop a pseudo-annotation pipeline that takes signed video and English as input and outputs a ranked set of likely annotations, including time intervals, for glosses, fingerspelled words, and sign classifiers. Our pipeline uses sparse predictions from our fingerspelling recognizer and isolated sign recognizer (ISR), along with a K-Shot LLM approach, to estimate these annotations. In service of this pipeline, we establish simple yet effective baseline fingerspelling and ISR models, achieving state-of-the-art on FSBoard (6.7% CER) and on ASL Citizen datasets (74% top-1 accuracy). To validate and provide a gold-standard benchmark, a professional interpreter annotated nearly 500 videos from ASL STEM Wiki with sequence-level gloss labels containing glosses, classifiers, and fingerspelling signs. These human annotations and over 300 hours of pseudo-annotations are being released in supplemental material.

Emma J. McDonnell, Ping Liu, Steven M. Goodman et al.

Real-time captioning is a critical accessibility tool for many d/Deaf and hard of hearing (DHH) people. While the vast majority of captioning work has focused on formal settings and technical innovations, in contrast, we investigate captioning for informal, interactive small-group conversations, which have a high degree of spontaneity and foster dynamic social interactions. This paper reports on semi-structured interviews and design probe activities we conducted with 15 DHH participants to understand their use of existing real-time captioning services and future design preferences for both in-person and remote small-group communication. We found that our participants' experiences of captioned small-group conversations are shaped by social, environmental, and technical considerations (e.g., interlocutors' pre-established relationships, the type of captioning displays available, and how far captions lag behind speech). When considering future captioning tools, participants were interested in greater feedback on non-speech elements of conversation (e.g., speaker identity, speech rate, volume) both for their personal use and to guide hearing interlocutors toward more accessible communication. We contribute a qualitative account of DHH people's real-time captioning experiences during small-group conversation and future design considerations to better support the groups being captioned, both in person and online.

Raja Kushalnagar, Gary Behm, Kevin Wolfe et al.

Students who are deaf and hard of hearing cannot hear in class and do not have full access to spoken information. They can use accommodations such as captions that display speech as text. However, compared with their hearing peers, the caption accommodations do not provide equal access, because they are focused on reading captions on their tablet and cannot see who is talking. This viewing isolation contributes to student frustration and risk of doing poorly or withdrawing from introductory engineering courses with lab components. It also contributes to their lack of inclusion and sense of belonging. We report on the evaluation of a Real-Time Text Display with Speaker-Identification, which displays the location of a speaker in a group (RTTD-ID). RTTD-ID aims to reduce frustration in identifying and following an active speaker when there are multiple speakers, e.g., in a lab. It has three different display schemes to identify the location of the active speaker, which helps deaf students in viewing both the speaker's words and the speaker's expression and actions. We evaluated three RTTD speaker identification methods: 1) traditional: captions stay in one place and viewers search for the speaker, 2) pointer: captions stay in one place, and a pointer to the speaker is displayed, and 3) pop-up: captions "pop-up" next to the speaker. We gathered both quantitative and qualitative information through evaluations with deaf and hard of hearing users. The users preferred the pointer identification method over the traditional and pop-up methods.

Raja Kushalnagar, Matthew Seita, Abraham Glasser

Deaf individuals face great challenges in today's society. It can be very difficult to be able to understand different forms of media without a sense of hearing. Many videos and movies found online today are not captioned, and even fewer have a supporting video with an interpreter. Also, even with a supporting interpreter video provided, information is still lost due to the inability to look at both the video and the interpreter simultaneously. To alleviate this issue, we came up with a tool called closed interpreting. Similar to closed captioning, it will be displayed with an online video and can be toggled on and off. However, the closed interpreter is also user-adjustable. Settings, such as interpreter size, transparency, and location, can be adjusted. Our goal with this study is to find out what deaf and hard of hearing viewers like about videos that come with interpreters, and whether the adjustability is beneficial.

Abraham Glasser, Kesavan Kushalnagar, Raja Kushalnagar

Many personal devices have transitioned from visual-controlled interfaces to speech-controlled interfaces to reduce costs and interactive friction, supported by the rapid growth in capabilities of speech-controlled interfaces, e.g., Amazon Echo or Apple's Siri. A consequence is that people who are deaf or hard of hearing (DHH) may be unable to use these speech-controlled devices. We show that deaf speech has a high error rate compared to hearing speech, in commercial speech-controlled interfaces. Deaf speech had approximately a 78% word error rate (WER) compared to a hearing speech 18% WER. Our findings show that current speech-controlled interfaces are not usable by DHH people. Based on our findings, significant advances in speech recognition software or alternative approaches will be needed for deaf use of speech-controlled interfaces. We show that current speech-controlled interfaces are not usable by DHH people.

Abraham Glasser, Kesavan Kushalnagar, Raja Kushalnagar

Many personal devices have transitioned from visual-controlled interfaces to speech-controlled interfaces to reduce device costs and interactive friction. This transition has been hastened by the increasing capabilities of speech-controlled interfaces, e.g., Amazon Echo or Apple's Siri. A consequence is that people who are deaf or hard of hearing (DHH) may be unable to use these speech-controlled devices. We show that deaf speech has a high error rate compared to hearing speech, in commercial speech-controlled interfaces. Deaf speech had approximately a 78% word error rate (WER) compared to a hearing speech 18% WER. Our findings show that current speech-controlled interfaces are not usable by deaf and hard of hearing people. Therefore, it might be wise to pursue other methods for deaf persons to deliver natural commands to computers.