language:
- en
- de
- es
- fr
- es
- zh
license:
- cc-by-nc-sa-3.0
multilinguality:
- multilingual
task_categories:
- automatic-speech-recognition
- text-to-speech
- text-to-audio
pretty_name: talkbank_4_stt
dataset_info:
features:
- name: audio
struct:
- name: bytes
dtype: binary
- name: path
dtype: string
- name: transcript
dtype: string
- name: language_code
dtype: string
- name: subset
dtype: string
- name: full_language
dtype: string
- name: switch_id
dtype: string
- name: segment_id
dtype: string
- name: transcript_filename
dtype: string
- name: audio_len_sec
dtype: int64
- name: orig_file_start
dtype: float64
- name: orig_file_end
dtype: float64
- name: channel
dtype: int64
splits:
- name: de_switch_train
num_bytes: 370364837
num_examples: 217
- name: de_switch_test
num_bytes: 66355623
num_examples: 37
- name: de_segment_train
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num_examples: 10355
- name: de_segment_test
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num_examples: 1847
- name: ja_switch_train
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num_examples: 273
- name: ja_switch_test
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num_examples: 52
- name: ja_segment_train
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num_examples: 14620
- name: ja_segment_test
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num_examples: 3153
- name: zh_switch_train
num_bytes: 435453051
num_examples: 314
- name: zh_switch_test
num_bytes: 81031328
num_examples: 62
- name: zh_segment_train
num_bytes: 13593465
num_examples: 1943
- name: zh_segment_test
num_bytes: 2209388
num_examples: 288
- name: en_switch_train
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num_examples: 369
- name: en_switch_test
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num_examples: 68
- name: en_segment_train
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num_examples: 30736
- name: en_segment_test
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num_examples: 4166
- name: es_switch_train
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num_examples: 488
- name: es_switch_test
num_bytes: 225061634
num_examples: 89
- name: es_segment_train
num_bytes: 662759917
num_examples: 55213
- name: es_segment_test
num_bytes: 95103746
num_examples: 7743
- name: fr_switch_train
num_bytes: 426622783
num_examples: 93
- name: fr_switch_test
num_bytes: 61436995
num_examples: 17
- name: fr_segment_train
num_bytes: 223181171
num_examples: 18639
- name: fr_segment_test
num_bytes: 33991480
num_examples: 3002
download_size: 13250672382
dataset_size: 6428823016
configs:
- config_name: default
data_files:
- split: de_switch_train
path: data/de_switch_train-*
- split: de_switch_test
path: data/de_switch_test-*
- split: de_segment_train
path: data/de_segment_train-*
- split: de_segment_test
path: data/de_segment_test-*
- split: ja_switch_train
path: data/ja_switch_train-*
- split: ja_switch_test
path: data/ja_switch_test-*
- split: ja_segment_train
path: data/ja_segment_train-*
- split: ja_segment_test
path: data/ja_segment_test-*
- split: zh_switch_train
path: data/zh_switch_train-*
- split: zh_switch_test
path: data/zh_switch_test-*
- split: zh_segment_train
path: data/zh_segment_train-*
- split: zh_segment_test
path: data/zh_segment_test-*
- split: en_switch_train
path: data/en_switch_train-*
- split: en_switch_test
path: data/en_switch_test-*
- split: en_segment_train
path: data/en_segment_train-*
- split: en_segment_test
path: data/en_segment_test-*
- split: es_switch_train
path: data/es_switch_train-*
- split: es_switch_test
path: data/es_switch_test-*
- split: es_segment_train
path: data/es_segment_train-*
- split: es_segment_test
path: data/es_segment_test-*
- split: fr_switch_train
path: data/fr_switch_train-*
- split: fr_switch_test
path: data/fr_switch_test-*
- split: fr_segment_train
path: data/fr_segment_train-*
- split: fr_segment_test
path: data/fr_segment_test-*
Dataset Card
Dataset Description
This dataset is a benchmark based on the TalkBank[1] corpus—a large multilingual repository of conversational speech that captures real-world, unstructured interactions. We use CA-Bank [2], which focuses on phone conversations between adults, which include natural speech phenomena such as laughter, pauses, and interjections. To ensure the dataset is highly accurate and suitable for benchmarking conversational ASR systems, we employ extensive set of pre-processing.
Preprocessing Steps
We apply the following preprocessing steps to ensure the dataset’s quality:
- Manual filtering of conversations
- Speaker-channel alignment
- Timestamp alignment using voice activity detection (VAD)
- Discarding segments based on Word Error Rate (WER) thresholds
Paper and Code Repository
For a comprehensive explanation of the preprocessing pipeline and dataset details, refer to our paper ASR Benchmarking: The Need for a More Representative Conversational Dataset and explore our GitHub repository for code and additional resources.
Segmentation Types: Speaker Switch vs Annotation
We offer two types of segmentation for this dataset:
- Annotation-based Segmentation: Segments are derived directly from the annotations provided in the original TalkBank corpus.
- Speaker Switch Segmentation: We consolidate consecutive segments from the same speaker into a single, larger audio segment, providing an alternative structure for analysis.
Citations
While using this dataset please cite:
@article{maheshwari2024asr,
title={ASR Benchmarking: Need for a More Representative Conversational Dataset},
author={Maheshwari, Gaurav and Ivanov, Dmitry and Johannet, Th{\'e}o and Haddad, Kevin El},
journal={arXiv preprint arXiv:2409.12042},
year={2024}
}
In addition, please acknowledge the TalkBank dataset::
@article{macwhinney2010transcribing,
title={Transcribing, searching and data sharing: The CLAN software and the TalkBank data repository},
author={MacWhinney, Brian and Wagner, Johannes},
journal={Gesprachsforschung: Online-Zeitschrift zur verbalen Interaktion},
volume={11},
pages={154},
year={2010},
publisher={NIH Public Access}
}
Licensing Information
This dataset is released under the CC BY-NC-SA 3.0.
References
[1]: MacWhinney, Brian. "TalkBank: Building an open unified multimodal database of communicative interaction." (2004). [2]: MacWhinney, Brian, and Johannes Wagner. "Transcribing, searching and data sharing: The CLAN software and the TalkBank data repository." Gesprachsforschung: Online-Zeitschrift zur verbalen Interaktion 11 (2010): 154.