HaessigDB: A Database of Irritable Speech with Intensity Grading
Journal
Interspeech 2026
Type
conference paper
Date Issued
2026-09-27
Author(s)
Abstract
We introduce HaessigDB, a curated collection of human-annotated emotional speech snippets with intensity grading. The database contains acted call-center style speech snippets with human ratings on three irritability dimensions, namely annoyance, frustration, and aggression. Each snippet is rated on an ordinal intensity scale, which supports fine-grained analysis beyond binary emotion detection. To improve annotation quality, we construct high-agreement subsets per dimension through iterative processing steps. The resulting corpus preserves temporal ordering, enabling research on irritability development in conversations and early escalation signals. HaessigDB addresses a data gap in speech-based emotion research for service settings, where intervention decisions depend on both intensity and temporal development. The database can support supervised learning, trajectory modeling, and design of escalation-aware conversational systems.
Funding(s)
voice
HSG Classification
contribution to scientific community
Refereed
Yes
Subject(s)
Division(s)
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Name
interspeech2026_haessigDB.pdf
Size
5.1 MB
Format
Adobe PDF
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