README.md

---
dataset_info:
  features:
  - name: audio
    dtype: audio
  - name: 'no'
    dtype: int64
  - name: en_speaker
    dtype: string
  - name: en_sentence
    dtype: string
  - name: en_spkid
    dtype: string
  - name: en_wav
    dtype: string
  - name: en_spk_gender
    dtype: string
  - name: en_spk_state
    dtype: string
  - name: scenario_id
    dtype: string
  - name: scenario_tag
    dtype: string
  - name: scenario_title
    dtype: string
  - name: scenario_original_language
    dtype: string
  - name: ja_speaker
    dtype: string
  - name: ja_sentence
    dtype: string
  - name: ja_spkid
    dtype: string
  - name: ja_wav
    dtype: string
  - name: ja_spk_gender
    dtype: string
  - name: ja_spk_prefecture
    dtype: string
  splits:
  - name: train
    num_bytes: 4857393677
    num_examples: 40000
  - name: validation
    num_bytes: 508034664.324
    num_examples: 4102
  - name: test
    num_bytes: 569876258.76
    num_examples: 4240
  download_size: 5807088419
  dataset_size: 5935304600.084001
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
license: cc-by-nc-sa-4.0
task_categories:
- translation
language:
- ja
- en
pretty_name: SpeechBSD
extra_gated_prompt: "Please enter your affiliation and agree to the following terms to use the dataset."
extra_gated_fields:
 Affiliation: text
 I accept the license: checkbox
 I agree to not attempt to determine the identity of speakers in this dataset: checkbox
---
# SpeechBSD Dataset

This is an extension of the [BSD corpus](https://github.com/tsuruoka-lab/BSD), a Japanese--English dialogue translation corpus, with audio files and speaker attribute information.
Although the primary intended usage is for speech-to-text translation, it can be viewed as text/speech Japanese/English/cross-language dialogue corpus and can be used for various tasks.

## Dataset Statistics

|           |  Train |  Dev. |  Test |
| --------- | ------:| -----:| -----:|
| Scenarios |    670 |    69 |    69 |
| Sentences | 20,000 | 2,051 | 2,120 |
| En audio (h) | 20.1 | 2.1 | 2.1 |
| Ja audio (h) | 25.3 | 2.7 | 2.7 |
| En audio gender (male % / female %) | 47.2 / 52.8 | 50.1 / 49.9 | 44.4 / 55.6 |
| Ja audio gender (male % / female %) | 68.0 / 32.0 | 62.3 / 37.7 | 69.0 / 31.0 |

## Data Structure

We also provide the dataset at [the GitHub repository](https://github.com/ku-nlp/speechBSD.git). The data structure there is similar to the original BSD corpus.
Here, the structure is changed to be represented in the JSONL format where one instance contains one audio file.

### Data Instances

There are two types of instances, one that contains English wav file, and one that contains Japanese wav file.

A typical instance that contains an English wav file looks like this:

```
{
  'audio': {'path': '/path/to/speech-bsd-hf/data/dev/190315_E001_17_spk0_no10_en.wav', 'array': array([ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
       -6.10351562e-05, -1.83105469e-04,  6.10351562e-05]), 'sampling_rate': 16000},
  'no': 10,
  'en_speaker': 'Mr. Ben Sherman',
  'en_sentence': 'You can also check out major institutions like banks, accounting companies, and market research companies.',
  'en_spkid': '190315_E001_17_spk0_en',
  'en_wav': '190315_E001_17_spk0_no10_en.wav',
  'en_spk_gender': 'M',
  'en_spk_state': 'CA',
  'scenario_id': '190315_E001_17',
  'scenario_tag': 'training',
  'scenario_title': 'Training: How to do research',
  'scenario_original_language': 'en',
  'ja_speaker': None,
  'ja_sentence': None,
  'ja_spkid': None,
  'ja_wav': None,
  'ja_spk_gender': None,
  'ja_spk_prefecture': None
}
```

In the corresponding instance that contains a Japanese wav file, `en_speaker`, `en_sentence`, `en_spkid`, `en_wav`, `en_spk_gender`, and `en_spk_state` are `None` and corresponding Japanese ones are filled instead.

### Data Fields

Each utterance is a part of a scenario.

The scenario information is shown with `scenario_id`, `scenario_tag`, `scenario_title`, and `scenario_original_language`, which corresponds respectively to `id`, `tag`, `title`, and `original_language` in the original BSD corpus.

The utterance-specific fields are the following:

- `no`, `ja_speaker`, `en_speaker`, `ja_sentence`, `en_sentence` are identical to the ones of the BSD corpus.
- `ja_spkid` and `en_spkid` show speaker IDs consistent throughout the conversation.
- `ja_wav` and `en_wav` show the wavfile names.
- `ja_spk_gender` and `en_spk_gender` show the gender of the speaker of the corresponding wav file (either "M" or "F").
- `ja_spk_prefecture` and `en_spk_state` show the speaker's region of origin.
- As any other huggingface audio datasets, `audio` contains full path to the audio file in your environment, audio array, and sampling rate (which is 16000). You will need `librosa` and `soundfile` packages to have access to these fields.

Here are some additional notes:

- Speakers are different if speaker ID is different.
  For example, if a conversation is spoken by two speakers taking turns, there would be 4 speakers (2 Japanese speakers and 2 English speakers).
  However, it's possible that speakers with different speaker ID is actually spoken by the same person because of the way audio is collected.

- Gender information of audio does not necessarily match with the one inferrable from text.
  For example, even if the `en_speaker` is "Mr. Sam Lee", the audio may contain female voice.
  This is because no explicit gender information is given in the original BSD corpus.

- Japanese speech is collected from Japanese speakers who are from Japan.
  - `ja_spk_prefecture` is one of the 47 prefectures or "不明" (unknown). Japanese prefectures have four different ending characters, "県", "府", "都", and "道".
    - Prefectures that ends with "県" or "府" does not contain those characters (e.g., "神奈川", "京都").
    - Tokyo is "東京" without "都".
    - Hokkaido is "北海道".

- English speech is collected from English speakers who are from the US.
  - `en_spk` is one of the 50 states, written in postal abbreviation.

## Citation

If you find the dataset useful, please cite our ACL 2023 Findings paper: [Towards Speech Dialogue Translation Mediating Speakers of Different Languages](https://aclanthology.org/2023.findings-acl.72/).

```
@inproceedings{shimizu-etal-2023-towards,
  title = "Towards Speech Dialogue Translation Mediating Speakers of Different Languages",
  author = "Shimizu, Shuichiro  and
    Chu, Chenhui  and
    Li, Sheng  and
    Kurohashi, Sadao",
  booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
  month = jul,
  year = "2023",
  address = "Toronto, Canada",
  publisher = "Association for Computational Linguistics",
  url = "https://aclanthology.org/2023.findings-acl.72",
  pages = "1122--1134",
  abstract = "We present a new task, speech dialogue translation mediating speakers of different languages. We construct the SpeechBSD dataset for the task and conduct baseline experiments. Furthermore, we consider context to be an important aspect that needs to be addressed in this task and propose two ways of utilizing context, namely monolingual context and bilingual context. We conduct cascaded speech translation experiments using Whisper and mBART, and show that bilingual context performs better in our settings.",
}
```

## License

This dataset is licensed under [CC-BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).