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This paper explores a multimodal approach for translating emotional cues present in speech, designed with Deaf and Hard-of-Hearing (dhh) individuals in mind. Prior work has focused on visual cues applied to captions, successfully conveying whether a speaker’s words have a negative or positive tone (valence), but with mixed results regarding the intensity (arousal) of these emotions. We propose a novel method using haptic feedback to communicate a speaker’s arousal levels through vibrations on a wrist-worn device. In a formative study with 16 dhh participants, we tested six haptic patterns and found that participants preferred single per-word vibrations at 75 Hz to encode arousal. In a follow-up study with 27 dhh participants, this pattern was paired with visual cues, and narrative engagement with audio-visual content was measured. Results indicate that combining haptics with visuals significantly increased engagement compared to a conventional captioning baseline and a visuals-only affective captioning style. 

Affective captions employ visual typographic modulations to convey a speaker’s emotions, improving speech accessibility for Deaf and Hard-of-Hearing (dhh) individuals. However, the most effective visual modulations for expressing emotions remain uncertain. Bridging this gap, we ran three studies with 39 dhh participants, exploring the design space of affective captions, which include parameters like text color, boldness, size, and so on. Study 1 assessed preferences for nine of these styles, each conveying either valence or arousal separately. Study 2 combined Study 1’s top-performing styles and measured preferences for captions depicting both valence and arousal simultaneously. Participants outlined readability, minimal distraction, intuitiveness, and emotional clarity as key factors behind their choices. In Study 3, these factors and an emotion-recognition task were used to compare how Study 2’s winning styles performed versus a non-styled baseline. Based on our findings, we present the two best-performing styles as design recommendations for applications employing affective captions. 

As many as three million school age children between the ages of 5 and 14 years, live with severe to profound hearing loss in Nigeria. Many of these Deaf or Hard of Hearing (DHH) children developed their hearing loss later in life, non-congenitally, hence their parents are hearing. While their teachers in the Deaf schools they attend can often communicate effectively with them in dialects of American Sign Language (ASL), the unofficial sign lingua franca in Nigeria, communication at home with other family members is challenging and sometimes non-existent. This results in adverse social consequences including stigmatization, for the students.With the recent successes of AI in natural language understanding, the goal of automated sign language understanding is becoming more realistic using neural deep learning technologies. To this effect, the proposed project aims at co-designing and developing an ongoing AI-driven two-way sign language interpretation tool that can be deployed in homes, to improve language accessibility and communication between the DHH students and other family members. This ensures inclusive and equitable social interactions and can promote lifelong learning opportunities for them outside of the school environment.