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| Content Provider | ACM Digital Library |
|---|---|
| Author | Bredin, Hervé Stefas, Mickaël Poignant, Johann Tamisier, Thomas Barras, Claude Bruneau, Pierrick |
| Abstract | Classification quality criteria such as precision, recall, and F-measure are generally the basis for evaluating contributions in automatic speaker recognition. Specifically, comparisons are carried out mostly via mean values estimated on a set of media. Whilst this approach is relevant to assess improvement w.r.t. the state-of-the-art, or ranking participants in the context of an automatic annotation challenge, it gives little insight to system designers in terms of cues for improving algorithms, hypothesis formulation, and evidence display. This paper presents a design study of a visual and interactive approach to analyze errors made by automatic annotation algorithms. A timeline-based tool emerged from prior steps of this study. A critical review, driven by user interviews, exposes caveats and refines user objectives. The next step of the study is then initiated by sketching designs combining elements of the current prototype to principles newly identified as relevant. |
| Starting Page | 323 |
| Ending Page | 326 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781450339124 |
| DOI | 10.1145/2818346.2820769 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2015-11-09 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Speaker identification Visual analytics |
| Content Type | Text |
| Resource Type | Article |
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