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Confides: A Visual Analytics Solution for Automated Speech Recognition Analysis and Exploration

Year: 2024

Type: conference-paper

Abstract: Confidence scores of automatic speech recognition (ASR) outputs are often inadequately communicated, preventing its seamless integration into analytical workflows. In this paper, we introduce Confides, a visual analytic system developed in collaboration with intelligence analysts to address this issue. Confides aims to aid exploration and post-AI-transcription editing by visually representing the confidence associated with the transcription. We demonstrate how our tool can assist intelligence analysts who use ASR outputs in their analytical and exploratory tasks and how it can help mitigate misinterpretation of crucial information. We also discuss opportunities for improving textual data cleaning and model transparency for human-machine collaboration.

Source: IEEE Visualization and Visual Analytics (VIS)

DOI: 10.1109/vis55277.2024.00062

Authors: Sunwoo Ha, Chaehun Lim, R. Jordan Crouser, Alvitta Ottley

Language: English

Topic: Data Visualization and Analytics

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, U.S. Department of Defense

Awards: HDR Institute: Institute for Data Driven Dynamical Design, CAREER: Context-Aware Visual Analytics Systems: Evolving the One-Size-Fits-All Approach to Design and Evaluation

OpenAlex ID: W4404916508

FWCI:

Cites: 27

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Related to: 10