Year: 2026
Type: conference-paper
Abstract: Although widely used in dialog act prediction and generation, the Switchboard Dialog Act (SwDA) corpus has performed poorly in models incorporating prosodic information because of misalignment between speech and text data.In this paper, we report our completion of the work begun in Chen et al. (2024) in addressing these misalignment issues with an improved SwDA corpus called RASwDA (Re-Aligned Switchboard Dialog Act Corpus).Now fully realigned and validated, RASwDA finally meets standards of accuracy allowing for classification models trained on it to exceed classification benchmarks set by models trained on other Switchboard subcorpora.
Source: Linguistic Annotation Workshop
Institutions: Columbia University, University of Alberta, Google (United States)
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166863993
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