README.md

metadata

license: cc-by-sa-4.0
language:
  - en
task_categories:
  - question-answering
size_categories:
  - 10K<n<100K
tags:
  - multi-hop-qa
  - retrieval-augmented-generation
  - speech-recognition
  - asr-robustness
  - accented-speech
configs:
  - config_name: hotpotqa
    data_files:
      - split: test
        path: hotpotqa.jsonl
  - config_name: 2wikimultihopqa
    data_files:
      - split: test
        path: 2wikimultihopqa.jsonl
  - config_name: musique
    data_files:
      - split: test
        path: musique.jsonl

Spoken Multi-hop QA: ASR Transcripts Across Four English Accents

ASR transcriptions of 3,000 multi-hop QA questions, each spoken in four English accents and transcribed with Whisper-large-v3. Released as the data companion to Better Retrieval, Worse Robustness: How Multi-hop RAG Amplifies Upstream ASR Errors (EMNLP 2026, Main Conference).

The dataset exists to make one thing cheap to study: what happens to a retrieval pipeline when its query arrives through ASR rather than as clean text. Every row pairs a gold question (the clean-text oracle input) with the transcription actually produced for one accent, so the degradation is directly measurable without re-running any speech model.

Code: https://github.com/Continuum-AI-Corp/spoken-multihop-rag

Contents

Config	Questions	Rows	Question types
hotpotqa	1,000	4,000	bridge, comparison
2wikimultihopqa	1,000	4,000	+ compositional, inference
musique	1,000	4,000	2-, 3-, 4-hop compositions

One row per (question, accent). Questions are sampled uniformly at random from each benchmark's official validation set with seed 42.

Schema

Field	Type	Description
id	string	Question id from the source benchmark
benchmark	string	hotpotqa, 2wikimultihopqa, musique
accent	string	us, in, ph, ng
voice	string	Edge TTS voice used to synthesize this row
question	string	Gold question text, i.e. the clean-text oracle input
answer	string	Gold answer from the source benchmark
transcription	string	Whisper-large-v3 top-1 output. null when tts_failed
wer	float	Word error rate of transcription against question. null when tts_failed
tts_failed	bool	true for four rows whose speech synthesis produced an empty file

wer uses unit-cost Levenshtein alignment over lowercased, whitespace-tokenized words, with no punctuation stripping. This matches the implementation used for every number in the paper (evaluation/core/metrics.py).

Mean WER

Config	US	IN	PH	NG
hotpotqa	9.4%	11.0%	10.6%	14.5%
2wikimultihopqa	13.6%	14.1%	13.8%	17.1%
musique	5.2%	5.5%	5.7%	8.0%

Usage

from datasets import load_dataset

ds = load_dataset("orcarouter/spoken-multihop-rag", "2wikimultihopqa", split="test")

# clean-text vs ASR input for one accent
ng = ds.filter(lambda r: r["accent"] == "ng" and not r["tts_failed"])
print(ng[0]["question"])       # oracle input
print(ng[0]["transcription"])  # what the retriever actually receives
print(ng[0]["wer"])

How it was built

Speech synthesis. Each question was synthesized with Microsoft Edge TTS at 24 kHz, one voice per accent:

Accent	Voice	Role
US	en-US-JennyNeural	High-resource baseline
IN	en-IN-NeerjaNeural	Non-native (Indian English)
PH	en-PH-RosaNeural	Non-native (Filipino English)
NG	en-NG-EzinneNeural	Low-resource (Nigerian English)

Transcription. Whisper-large-v3, English forced, without timestamps.

Limitations

Read these before using the dataset as an ASR benchmark.

• No audio. Only transcripts are released. The synthesized speech is Microsoft Edge TTS output and is not redistributed here. Audio can be regenerated with data/build_dataset.py in the code repository, though the service is not version-pinned, so regenerated audio and any transcripts derived from it will not match these byte for byte.

• One voice per accent. Accent is varied, speaker is not. Results should be read as a controlled probe of accent-induced ASR error, not as an estimate of population-level performance for any speaker group.

• Synthetic speech understates real difficulty. On a public corpus of real Nigerian-accented English, Whisper-large-v3 reaches 28.9% mean WER, against 17.1% / 14.5% / 7.9% for the synthesized Nigerian condition on 2WikiMultiHopQA / HotpotQA / MuSiQue. The synthesized setting is easier by a factor of roughly 1.7x to 3.7x.

• Four rows are unusable. Speech synthesis returned an empty file for the four (question, accent) pairs listed below, so no transcription exists for them. They carry tts_failed: true with null transcription and WER, which keeps them out of aggregate statistics.

  Config	Accent	id
  hotpotqa	ng	5a8603f655429960ec39b601
  hotpotqa	ph	5ade063d5542997dc7907129
  2wikimultihopqa	ng	69a429bb097911ebbdb0ac1f6bf848b6
  musique	ng	3hop2__326964_36852_7713

• English only. No claim is made about other languages.

Licensing and attribution

This dataset is released under CC BY-SA 4.0, inherited from the most restrictive upstream source. Each row's question and answer are redistributed from one of three benchmarks:

Source	License	Reference
HotpotQA	CC BY-SA 4.0	hotpotqa.github.io
2WikiMultiHopQA	Apache-2.0	Alab-NII/2wikimultihop
MuSiQue	CC BY 4.0	StonyBrookNLP/musique

HotpotQA's ShareAlike term propagates to derivative works, which is why the whole dataset carries CC BY-SA 4.0 rather than a per-config license. The transcription field holds Whisper-large-v3 output; Whisper is MIT-licensed. Accompanying code is Apache-2.0.

If you redistribute or build on this dataset, keep the attributions above and license derivatives compatibly.

Citation

@inproceedings{bao2026better,
  title     = {Better Retrieval, Worse Robustness: How Multi-hop {RAG} Amplifies Upstream {ASR} Errors},
  author    = {Bao, Zhenghua},
  booktitle = {Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing},
  year      = {2026},
  note      = {To appear},
  url       = {https://arxiv.org/abs/2608.22872}
}

Please also cite the source benchmark you use, and Whisper (Radford et al., 2023) for the transcriptions.