End-to-end spoken language understanding (SLU) predicts intent directly from
audio using a single model. It promises to improve the performance of assistant
systems by leveraging acoustic information lost in the intermediate textual
representation and preventing cascading errors from Automatic Speech
Recognition (ASR). Further, having one unified model has efficiency advantages
when deploying assistant systems on-device. However, the limited number of
public audio datasets with semantic parse labels hinders the research progress
in this area. In this paper, we release the Spoken Task-Oriented semantic
Parsing (STOP) dataset, the largest and most complex SLU dataset to be publicly
available. Additionally, we define low-resource splits to establish a benchmark
for improving SLU when limited labeled data is available. Furthermore, in
addition to the human-recorded audio, we are releasing a TTS-generated version
to benchmark the performance for low-resource domain adaptation of end-to-end
SLU systems. Initial experimentation show end-to-end SLU models performing
slightly worse than their cascaded counterparts, which we hope encourages
future work in this direction.

本文发布了 Spoken Task-Oriented semantic Parsing (STOP) 数据集，这是目前公开可用的最大、最复杂的 SLU 数据集，评估了在有限标注数据情况下改进 SLU 的基于端到端 SLU 系统的低资源领域适应性的性能，并表明端到端 SLU 模型的性能不及级联模型。

STOP：面向口语任务的语义解析数据集

STOP: A dataset for Spoken Task Oriented Semantic Parsing

Whereas conventional spoken language understanding (SLU) systems map speech
to text, and then text to intent, end-to-end SLU systems map speech directly to
intent through a single trainable model. Achieving high accuracy with these
end-to-end models without a large amount of training data is difficult. We
propose a method to reduce the data requirements of end-to-end SLU in which the
model is first pre-trained to predict words and phonemes, thus learning good
features for SLU. We introduce a new SLU dataset, Fluent Speech Commands, and
show that our method improves performance both when the full dataset is used
for training and when only a small subset is used. We also describe preliminary
experiments to gauge the model's ability to generalize to new phrases not heard
during training.

本研究提出了一种新的减少训练数据量的语音理解模型，在预训练中通过预测单词和音素来学习关键特征，并使用新的 SLU 数据集 Fluent Speech Commands 进行实验以测试泛化能力。