Datasets:

ytdata
/

vitw-context-select

license: unknown
language:
  - en
task_categories:
  - automatic-speech-recognition
pretty_name: VITW Context-Select (EN)
tags:
  - speech
  - asr
  - context
  - robustness
configs:
  - config_name: strict
    default: true
    data_files:
      - split: validation
        path: data/strict/validation-*.parquet
      - split: test
        path: data/strict/test-*.parquet
  - config_name: prefix
    data_files:
      - split: validation
        path: data/prefix/validation-*.parquet
      - split: test
        path: data/prefix/test-*.parquet

VITW Context-Select (EN)

A selected subset of the original English audio of zhifeixie/Voices-in-the-Wild-Bench (revision 788f5d72c6b0e9091b5c2e432370923b6f9f0660). No noise or other processing was added; audio bytes are the upstream WAV files (24 kHz mono PCM16), SHA-256 checked.

Purpose: test whether an ASR system (e.g. an MoE-fusion ASR with an LLM branch) uses preceding sentence context to recover words that are acoustically hard to recognise.

Configs

config	rule (per target word; an utterance is kept if ≥1 word qualifies)	validation	test
`strict` (default)	acoustically ambiguous vs a near-sounding word and the disambiguating context is in the prefix and Whisper large-v3 (no prompt) actually misrecognised the word (number-format / spelling variants such as 7/seven, centre/center are not counted as errors)	85 utts / 0.20 h / 84 groups	372 utts / 0.90 h / 365 groups
`prefix`	acoustically ambiguous and context in the prefix (no Whisper-error requirement)	283 utts / 0.65 h / 266 groups	1,076 utts / 2.61 h / 990 groups

validation = dev partition (used for threshold design); test = held-out partition. Splits are made at template-group level (near-duplicate sentences never cross splits), seed 20261108.

from datasets import load_dataset
test = load_dataset("ytdata/vitw-context-select", "strict", split="test")
# only the test files:
test = load_dataset("ytdata/vitw-context-select", data_files={"test": "data/strict/test-*.parquet"}, split="test")

How words were selected (machine screening, no human listening yet)

Candidates (text only): every reference word with a homophone / near-phone alternative from CMUdict (e.g. leak / league, seats / seeds), alternatives with wordfreq zipf ≥ 2.5. Function words excluded.
Acoustic ambiguity: Whisper large-v3 forced-choice on a ±0.3 s crop around the word; ambiguous if the best near-sounding alternative is within e^1 of the target, or Whisper's free decoding got the word wrong. Exact homophones are not counted as acoustic evidence.
Context: Qwen2.5-7B-Instruct scores the reference word vs alternatives given the prefix (and full sentence); kept when the reference is ≥10× more likely than the hardest alternative and the context adds ≥e^1 beyond the word's prior.

Rule file and all scores are in the build repo (configs/selection_rules.yaml, selection v1).

Columns

audio, sample_id, name, file_name, original_split (real_*/syn_* condition), source_type, condition, mixed_components, reference_raw, reference_normalized, group_id, partition, duration_sec, audio_sha256, n_targets, selector_hypothesis (Whisper large-v3 greedy output), and targets: JSON list, one entry per qualifying word with target_text, target_char_span / target_word_span (half-open), target_time_sec (approximate, from Whisper word timestamps, unverified), alternatives, selector_target_word, crop_margin, context_availability, prefix_margin, prefix_shift, full_margin, hardest_alternative_prefix.

Limitations

Labels are provisional (machine-screened); nobody has listened to the audio yet.
strict is enriched for errors of one specific model (Whisper large-v3); it is "hard for that selector", not representative of all ASR or of the full benchmark. Report full-benchmark results separately.
Whisper has an internal LM, so "acoustic ambiguity" is a proxy, not ground truth.
Text overlap was checked only against the Voices-in-the-Wild-2M English text manifest (no exact or near matches found); overlap with other training data is unknown.
Source sentences are short, highly predictable everyday sentences; low-context counterexamples are rare.
One truncated upstream clip (vitw_04834_real_en_far_field, 0.08 s) is excluded.

中文摘要

從 Voices-in-the-Wild-Bench 英文原始音檔（未加任何噪音）挑出「目標詞聽起來易混淆、但前文上下文可判斷」的句子。strict 另要求 Whisper large-v3 實際聽錯該詞（test 372 句）；prefix 較寬（test 1,076 句）。 validation 為 dev（已用於門檻設計），結論請以 test 報告。標籤為機器初篩，尚無人工聽音驗證。