Detection of Self-Introductions in Legislative Testimony
Self-introductions are common in legislative committee testimonies. Successfully detecting them and extracting the speaker's name is enormously helpful in the task of speaker identification in the context of government meetings. In this paper, we present a pipeline for detection of self-introductions in legislative committee testimony using machine learning. We construct a training dataset from 1.54 million utterances spanning five state legislative sessions, apply a name-matching heuristic to generate automatic labels, and train three classifiers: a decision tree, random forest, and XGBoost to find self-introductions and extract the speaker's name. We construct a feature set combining ba...
| Authors | Sofija Dimitrijevic, Pallavi Das, Kasey Liu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 8 August 2026 |
| Last updated at source | 8 August 2026 |
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