  Progress in speech processing has been facilitated by shared datasets and
benchmarks. Historically these have focused on automatic speech recognition
(ASR), speaker identification, or other lower-level tasks. Interest has been
growing in higher-level spoken language understanding tasks, including using
end-to-end models, but there are fewer annotated datasets for such tasks. At
the same time, recent work shows the possibility of pre-training generic
representations and then fine-tuning for several tasks using relatively little
labeled data. We propose to create a suite of benchmark tasks for Spoken
Language Understanding Evaluation (SLUE) consisting of limited-size labeled
training sets and corresponding evaluation sets. This resource would allow the
research community to track progress, evaluate pre-trained representations for
higher-level tasks, and study open questions such as the utility of pipeline
versus end-to-end approaches. We present the first phase of the SLUE benchmark
suite, consisting of named entity recognition, sentiment analysis, and ASR on
the corresponding datasets. We focus on naturally produced (not read or
synthesized) speech, and freely available datasets. We provide new
transcriptions and annotations on subsets of the VoxCeleb and VoxPopuli
datasets, evaluation metrics and results for baseline models, and an
open-source toolkit to reproduce the baselines and evaluate new models.
