mlsanityData to 7 Oct 2026

HeySQuAD: A Spoken Question Answering Dataset

arXiv 2304.13689 ↗First posted 26 Apr 2023cs.CL cs.AIcited by 16

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Abstract (arXiv)

Spoken question answering (SQA) systems are critical for digital assistants and other real-world use cases, but evaluating their performance is a challenge due to the importance of human-spoken questions. This study presents a new large-scale community-shared SQA dataset called HeySQuAD, which includes 76k human-spoken questions, 97k machine-generated questions, and their corresponding textual answers from the SQuAD QA dataset. Our goal is to measure the ability of machines to accurately understand noisy spoken questions and provide reliable answers. Through extensive testing, we demonstrate that training with transcribed human-spoken and original SQuAD questions leads to a significant improvement (12.51%) in answering human-spoken questions compared to training with only the original SQuAD textual questions. Moreover, evaluating with a higher-quality transcription can lead to a further improvement of 2.03%. This research has significant implications for the development of SQA systems and their ability to meet the needs of users in real-world scenarios.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 18 of its 28 references that are in the map. First posted on arXiv 26 Apr 2023.

Builds on

18 of 28 references.

References · 18

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  3. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  4. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
  5. 2019fairseq: A Fast, Extensible Toolkit for Sequence Modelingcited by 1,486
  6. 2020fairseq S2T: Fast Speech-to-Text Modeling with fairseqcited by 174
  7. 2018Spoken SQuAD: A Study of Mitigating the Impact of Speech Recognition Errors on Listening Comprehensioncited by 74
  8. 2021Self-supervised Contrastive Cross-Modality Representation Learning for Spoken Question Answeringcited by 39
  9. 2021SD-QA: Spoken Dialectal Question Answering for the Real Worldcited by 36
  10. 2020Towards Data Distillation for End-to-end Spoken Conversational Question Answeringcited by 34
  11. 2022SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding Taskscited by 34
  12. 2020Knowledge Distillation for Improved Accuracy in Spoken Question Answeringcited by 31
6 more
  1. 2021Self-supervised Dialogue Learning for Spoken Conversational Question Answeringcited by 27
  2. 2020Contextualized Attention-based Knowledge Transfer for Spoken Conversational Question Answeringcited by 26
  3. 2016Towards Machine Comprehension of Spoken Content: Initial TOEFL Listening Comprehension Test by Machinecited by 23
  4. 2018ODSQA: Open-domain Spoken Question Answering Datasetcited by 21
  5. 2019SpeechBERT: An Audio-and-text Jointly Learned Language Model for End-to-end Spoken Question Answeringcited by 11
  6. 2019Mitigating the Impact of Speech Recognition Errors on Spoken Question Answering by Adversarial Domain Adaptationcited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 6; 2026: 8.

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  1. 2024SD-Eval: A Benchmark Dataset for Spoken Dialogue Understanding Beyond Wordscited by 67
  2. 2024Distilling an End-to-End Voice Assistant Without Instruction Training Datacited by 35
  3. 2025Mind the Gap! Static and Interactive Evaluations of Large Audio Modelscited by 8
  4. 2025SpokenNativQA: Multilingual Everyday Spoken Queries for LLMscited by 8
  5. 2025WearVox: An Egocentric Multichannel Voice Assistant Benchmark for Wearablescited by 7
  6. 2025The Role of Prosody in Spoken Question Answeringcited by 6
  7. 2025Spoken question answering for visual queriescited by 1
  8. 2026ALARM: Audio-Language Alignment for Reasoning Modelscited by 1
  9. 2026AuRA: Internalizing Audio Understanding into LLMs as LoRAcited by 1
  10. 2025Synera: Synergistic LLM Serving across Device and Cloud at Scalecited by 0
  11. 2026ATIR: Towards Audio-Text Interleaved Contextual Retrievalcited by 0
  12. 2026KVoiceBench, KOpenAudioBench, and KMMAU: Agent-Driven Korean Speech Benchmarks for Evaluating SpeechLMscited by 0
4 more
  1. 2026LuxSQA: Ask Me in Luxembourgish with TTS-Augmented Spoken Question Answeringcited by 0
  2. 2026Don't Just Listen, Try Planning: Graph-based Retrieval-Generation Agent for Long-form Audio Meeting Understandingcited by 0
  3. 2026The Bairong System for MLC-SLM 2026: Dynamic Question-Aware Evidence Routing for Multilingual Conversational Speech Understandingcited by 0
  4. 2026Omni-Embed-Mini: Binding Modalities Without Forgetting via Dense Distillationcited by 0