Datasets:
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configs:
- config_name: mm_contextasr
data_files:
- split: test
path: data/mm_contextasr/test.jsonl
- config_name: cv_yue
data_files:
- split: test
path: data/cv_yue/test.jsonl
- config_name: kespeech
data_files:
- split: test
path: data/kespeech/test.jsonl
- config_name: alimeeting
data_files:
- split: test
path: data/alimeeting/test.jsonl
language:
- zh
- yue
task_categories:
- automatic-speech-recognition
pretty_name: MM-ContextASR Bench
license: other
---
# MM-ContextASR Bench
Metadata and evaluation splits for **Multimodal Conversational Context for
LLM-Based ASR: Data Construction, Training, and Benchmark**.
## Dataset summary
| Config | Examples | Audio | Context | Primary metric |
| --- | ---: | --- | --- | --- |
| `mm_contextasr` | 1,250 (250 current utterances × 5 histories) | 1,439 WAV files included | Controlled user-assistant dialogue | entity Recall |
| `kespeech` | 19,212 | Source ID only | Same-speaker speech and transcript | CER, SER, entity Recall |
| `cv_yue` | 3,525 | Source ID only | Same-speaker speech and transcript | CER, SER, entity Recall |
| `alimeeting` | 2,850 | Source segment ID and timing only | Same-speaker far-field enrollment | target-only CER, SER |
The companion [MM-ContextASR](https://github.com/llh666521/MM-ContextASR) repository
contains validation, four-mode input rendering, and scoring tools.
The three external-corpus configurations contain metadata and source-audio
identifiers only; obtain their audio from the corresponding official releases.
## MM-ContextASR scenarios
Every aligned group fixes current speech, reference transcription, and target
entity while changing the preceding dialogue.
| Scenario | Historical evidence | Diagnostic purpose |
| --- | --- | --- |
| Irrelevant | Unrelated topic | Ignore distractors |
| Implicit | Related topic without the entity | Use indirect semantic cues |
| Explicit | Correct entity appears | Use direct contextual evidence |
| Correction | Historical ASR error corrected by assistant | Recover from transcript errors |
| Repeated Error | Historical error repeated by assistant | Resist error propagation |
## Record format
Each JSONL record contains a stable `id`, source dataset and split, task,
language, current audio ID, current reference, ordered history turns, and source
provenance. Task-specific fields include scenario/entity labels, accent,
speaker/meeting IDs, timestamps, and overlap statistics.
MM-ContextASR records use repository-relative paths under `audio/`. Audio fields
in the three external evaluation tracks are identifiers only. No internal
object-store URI, signed URL, or machine-specific path is included.
```json
{
"id": "common_voice_yue_31189594__common_voice_yue_31189595",
"dataset": "cv_yue",
"split": "test",
"task": "asr",
"language": "yue",
"current_audio_id": "common_voice_yue_31189595.mp3",
"current_transcript": "官僚兼地主",
"history": [
{
"role": "user",
"audio_id": "common_voice_yue_31189594.mp3",
"text": "睇我几有礼貌，去之前讲返声"
}
],
"speaker_id": "cv_yue_spk_0210a731f5caa5d4",
"normalization": "OpenCC-t2s",
"entities": [],
"source": {
"corpus": "Mozilla Common Voice",
"release": "26.0 (2026-06-12)",
"locale": "yue",
"license": "CC0-1.0"
}
}
```
## Context modes
The canonical row contains all available history. The toolkit renders:
| Mode | Historical user speech | Historical user text | Assistant text |
| --- | :---: | :---: | :---: |
| No Context | ✗ | ✗ | ✗ |
| Text-only | ✗ | ✓ | ✓ |
| Speech-only | ✓ | ✗ | ✓ |
| Speech+Text | ✓ | ✓ | ✓ |
Current-turn references, entity labels, and scenario labels are evaluation-only
and must never be included in prompts.
## Source-audio mapping
The external-corpus JSONL files preserve source identifiers but do not
redistribute the corresponding audio. Resolve each record as follows:
| Configuration | Mapping rule | Speaker relation |
| --- | --- | --- |
| `kespeech` | Match the basename in `current_audio_id` and `history[].audio_id` against the official KeSpeech release. For example, locate `1000043_6674a7fd.wav` recursively under the corpus audio root; released basenames are unique within this evaluation set. | Both files share the row-level `speaker_id`; the speaker prefix in each filename provides an additional check. |
| `cv_yue` | Download Common Voice Cantonese 26.0 and resolve each ID as `clips/<audio_id>`, for example `clips/common_voice_yue_31189595.mp3`. | The privacy-preserving row-level `speaker_id` is shared by the current and history clips. It is stable within this release but is not the original Common Voice `client_id`. |
| `alimeeting` | Resolve the raw SLR119 recording as `Eval_Ali_far/audio_dir/<meeting_id>_<far_channel>.wav`, then extract `[current_start, current_end]` and `[history_start, history_end]`. The two derived clips intentionally share a basename but come from `current_far/` and `reference_far/`; `current_audio_id` is not a raw SLR119 filename. | Both intervals belong to the row-level target `speaker_id`. The history interval is a same-speaker enrollment/reference segment and is not necessarily earlier than the current interval. |
For `mm_contextasr`, audio is included directly: `current_audio` and
`history[].audio` are repository-relative paths under `audio/current/` and
`audio/history/`. In every external-corpus configuration, `speaker_id` denotes
the identity shared by the current utterance and its user-speech context;
assistant turns do not carry a speaker identity.
## Evaluation
Use corpus-level CER and utterance-level SER after Chinese punctuation and
whitespace normalization. CV-Yue additionally applies OpenCC `t2s` to references
and predictions. AliMeeting references contain target-speaker words only.
For MM-ContextASR, report exact entity Recall per scenario and overall. The five
examples in a group share current audio and are paired observations.
For CV-Yue entity Recall, use the provided 395 proper-name and domain-term spans
covering 327 utterances. The annotations were extracted from references and
audited under fixed span and type rules; they form a targeted diagnostic set
rather than exhaustive entity annotation.
## Licensing and citation
See [LICENSES.md](LICENSES.md) for per-config terms. Cite the paper and each
source corpus used in your evaluation. The final paper citation will be added
when an identifier is available.
|