A Comparative Analysis of Automatic Speech Recognition Errors in Small Group Classroom Discourse

Cao, Jie; Ganesh, Ananya; Cai, Jon; Southwell, Rosy; Perkoff, E. Margaret; Regan, Michael W.; Kann, Katharina; Martin, James H.; Palmer, Martha; D’Mello, Sidney K.

doi:10.1145/3565472.3595606

Cited by 10 publications

(1 citation statement)

References 55 publications

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“…The second limitation relates to the errors in speech recognition. Namely, although with continuous retraining of the acoustic model the WER decreases, the WER might not be a realistic representation of what happens in the wild [ 124 ]. As also observed during the initial tests in real-world environments, the challenges posed by factors such as background noise, speaker variations, and the presence of multiple dialects have significant impacts on the actual accuracy of the model, e.g., the batch WER vs. test WER in Table 1 .…”

Section: Discussionmentioning

confidence: 99%

Multilingual Framework for Risk Assessment and Symptom Tracking (MRAST)

Šafran,

Lin,

Nateqi

et al. 2024

Sensors

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The importance and value of real-world data in healthcare cannot be overstated because it offers a valuable source of insights into patient experiences. Traditional patient-reported experience and outcomes measures (PREMs/PROMs) often fall short in addressing the complexities of these experiences due to subjectivity and their inability to precisely target the questions asked. In contrast, diary recordings offer a promising solution. They can provide a comprehensive picture of psychological well-being, encompassing both psychological and physiological symptoms. This study explores how using advanced digital technologies, i.e., automatic speech recognition and natural language processing, can efficiently capture patient insights in oncology settings. We introduce the MRAST framework, a simplified way to collect, structure, and understand patient data using questionnaires and diary recordings. The framework was validated in a prospective study with 81 colorectal and 85 breast cancer survivors, of whom 37 were male and 129 were female. Overall, the patients evaluated the solution as well made; they found it easy to use and integrate into their daily routine. The majority (75.3%) of the cancer survivors participating in the study were willing to engage in health monitoring activities using digital wearable devices daily for an extended period. Throughout the study, there was a noticeable increase in the number of participants who perceived the system as having excellent usability. Despite some negative feedback, 44.44% of patients still rated the app’s usability as above satisfactory (i.e., 7.9 on 1–10 scale) and the experience with diary recording as above satisfactory (i.e., 7.0 on 1–10 scale). Overall, these findings also underscore the significance of user testing and continuous improvement in enhancing the usability and user acceptance of solutions like the MRAST framework. Overall, the automated extraction of information from diaries represents a pivotal step toward a more patient-centered approach, where healthcare decisions are based on real-world experiences and tailored to individual needs. The potential usefulness of such data is enormous, as it enables better measurement of everyday experiences and opens new avenues for patient-centered care.

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Section: Discussionmentioning

confidence: 99%

Multilingual Framework for Risk Assessment and Symptom Tracking (MRAST)

Šafran,

Lin,

Nateqi

et al. 2024

Sensors

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From learning optimization to learner flourishing: Reimagining AI in Education at the Institute for Student‐AI Teaming (iSAT)

D'Mello,

Biddy,

Breideband

et al. 2024

AI Magazine

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The Institute for Student‐AI Teaming (iSAT) addresses the foundational question: how to promote deep conceptual learning via rich socio‐collaborative learning experiences for all students?—a question that is ripe for AI‐based facilitation and has the potential to transform classrooms. We advance research in speech, computer vision, human‐agent teaming, computer‐supported collaborative learning, expansive co‐design, and the science of broadening participation to design and study next generation AI technologies (called AI Partners) embedded in student collaborative learning teams in coordination with teachers. Our institute ascribes to theoretical perspectives that aim to create a normative environment of widespread engagement through responsible design of technology, curriculum, and pedagogy in partnership with K–12 educators, racially diverse students, parents, and other community members.

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