SLUE Phase-2: A Benchmark Suite Of Diverse Spoken Language Understanding Tasks
2022 · Suwon Shon, Siddhant Arora, Chyi-Jiunn Lin, et al.
Abstract
Spoken language understanding (SLU) tasks have been studied for many decades in the speech research community, but have not received as much attention as lower-level tasks like speech and speaker recognition. In particular, there are not nearly as many SLU task benchmarks, and many of the existing ones use data that is not freely available to all researchers. Recent work has begun to introduce such benchmark datasets for several tasks. In this work, we introduce several new annotated SLU benchmark tasks based on freely available speech data, which complement existing benchmarks and address gaps in the SLU evaluation landscape. We contribute four tasks: question answering and summarization involve inference over longer speech sequences; named entity localization addresses the speech-specific task of locating the targeted content in the signal; dialog act classification identifies the function of a given speech utterance. We follow the blueprint of the Spoken Language Understanding Evalu
Authors
(none)
Tags
Stats
Related papers
- Unislu: Unified Spoken Language Understanding From Heterogeneous Cross-task Datasets (2025)0.00
- A Study On The Integration Of Pre-trained SSL, ASR, LM And SLU Models For Spoken Language Understanding (2022)8.09
- MMSU: A Massive Multi-task Spoken Language Understanding And Reasoning Benchmark (2025)2.29
- Text Is No More Enough! A Benchmark For Profile-based Spoken Language Understanding (2021)0.00
- End-to-end Spoken Language Understanding: Performance Analyses Of A Voice Command Task In A Low Resource Setting (2022)8.35
- Audiobench: A Universal Benchmark For Audio Large Language Models (2024)10.21
- Bridging The Gap Between Clean Data Training And Real-world Inference For Spoken Language Understanding (2021)0.00
- Recent Advances In End-to-end Spoken Language Understanding (2019)8.09