# STOP: A dataset for Spoken Task Oriented Semantic Parsing arXiv: https://arxiv.org/abs/2207.10643 ยท HTML: https://mlsanity.com/paper/2207.10643/ ## Key facts - Cited by 22 papers in the map (data through 14 Oct 2026). - Builds on 18 of its 25 references that are in the map. - First posted on arXiv 29 Jun 2022. ## Builds on (most cited first, 18 of 18) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019) - [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020) - [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014) - [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021) - [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019) - [LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech](https://mlsanity.com/paper/1904.02882/) (2019) - [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://mlsanity.com/paper/2106.06103/) (2021) - [Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces](https://mlsanity.com/paper/1805.10190/) (2018) - [SLURP: A Spoken Language Understanding Resource Package](https://mlsanity.com/paper/2011.13205/) (2020) - [Speech Model Pre-training for End-to-End Spoken Language Understanding](https://mlsanity.com/paper/1904.03670/) (2019) - [Cross-Lingual Transfer Learning for Multilingual Task Oriented Dialog](https://mlsanity.com/paper/1810.13327/) (2018) - [Semantic Parsing for Task Oriented Dialog using Hierarchical Representations](https://mlsanity.com/paper/1810.07942/) (2018) - [Towards end-to-end spoken language understanding](https://mlsanity.com/paper/1802.08395/) (2018) - [Rethinking Evaluation in ASR: Are Our Models Robust Enough?](https://mlsanity.com/paper/2010.11745/) (2020) - [Low-Resource Domain Adaptation for Compositional Task-Oriented Semantic Parsing](https://mlsanity.com/paper/2010.03546/) (2020) - [From Audio to Semantics: Approaches to end-to-end spoken language understanding](https://mlsanity.com/paper/1809.09190/) (2018) - [Conversational Semantic Parsing](https://mlsanity.com/paper/2009.13655/) (2020) ## Built on by (most cited first, 22 of 22) - [Distilling an End-to-End Voice Assistant Without Instruction Training Data](https://mlsanity.com/paper/2410.02678/) (2024) - [SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding Tasks](https://mlsanity.com/paper/2212.10525/) (2022) - [A Comparative Study on E-Branchformer vs Conformer in Speech Recognition, Translation, and Understanding Tasks](https://mlsanity.com/paper/2305.11073/) (2023) - [CWCL: Cross-Modal Transfer with Continuously Weighted Contrastive Loss](https://mlsanity.com/paper/2309.14580/) (2023) - [UniverSLU: Universal Spoken Language Understanding for Diverse Tasks with Natural Language Instructions](https://mlsanity.com/paper/2310.02973/) (2023) - [Audio2Tool: Speak, Call, Act -- A Dataset for Benchmarking Speech Tool Use](https://mlsanity.com/paper/2604.22821/) (2026) - [Improving Textless Spoken Language Understanding with Discrete Units as Intermediate Target](https://mlsanity.com/paper/2305.18096/) (2023) - [A Study on the Integration of Pipeline and E2E SLU systems for Spoken Semantic Parsing toward STOP Quality Challenge](https://mlsanity.com/paper/2305.01620/) (2023) - [Improving End-to-End SLU performance with Prosodic Attention and Distillation](https://mlsanity.com/paper/2305.08067/) (2023) - [Skit-S2I: An Indian Accented Speech to Intent dataset](https://mlsanity.com/paper/2212.13015/) (2022) - [DeRAGEC: Denoising Named Entity Candidates with Synthetic Rationale for ASR Error Correction](https://mlsanity.com/paper/2506.07510/) (2025) - [Integrating Pretrained ASR and LM to Perform Sequence Generation for Spoken Language Understanding](https://mlsanity.com/paper/2307.11005/) (2023) - [Modality Confidence Aware Training for Robust End-to-End Spoken Language Understanding](https://mlsanity.com/paper/2307.12134/) (2023) - [Augmenting text for spoken language understanding with Large Language Models](https://mlsanity.com/paper/2309.09390/) (2023) - [The Pipeline System of ASR and NLU with MLM-based Data Augmentation toward STOP Low-resource Challenge](https://mlsanity.com/paper/2305.01194/) (2023) - [Improving End-to-End Speech Processing by Efficient Text Data Utilization with Latent Synthesis](https://mlsanity.com/paper/2310.05374/) (2023) - [Multimodal Contextualized Semantic Parsing from Speech](https://mlsanity.com/paper/2406.06438/) (2024) - [PRoDeliberation: Parallel Robust Deliberation for End-to-End Spoken Language Understanding](https://mlsanity.com/paper/2406.07823/) (2024) - [Investigating Low-Cost LLM Annotation for~Spoken Dialogue Understanding Datasets](https://mlsanity.com/paper/2406.13269/) (2024) - [StarDrinks: An English and Korean Test Set for SLU Evaluation in a Drink Ordering Scenario](https://mlsanity.com/paper/2604.26500/) (2026) - [Do Spoken Language Models Hear Speech as They Read Text? Bridging Structural Gaps Between Speech and Text](https://mlsanity.com/paper/2608.22908/) (2026) - [Talk2Agent: Benchmarking Voice Interfaces for Text Agents](https://mlsanity.com/paper/2609.38867/) (2026) ## Abstract (arXiv) 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.