Datasets:

lilonghao
/

MM-ContextASR-Bench

---
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.

	---
	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.