| --- |
| language: |
| - en |
| task_categories: |
| - automatic-speech-recognition |
| - text-generation |
| - audio-classification |
| tags: |
| - spoken-dialogue |
| - full-duplex |
| - turn-taking |
| - interruption |
| - backchannel |
| pretty_name: InteractSpeech English Text and Timeline |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/*.parquet |
| --- |
| |
| # InteractSpeech: English Text-and-Timeline Release |
|
|
| This is the English text-and-timeline release associated with **InteractSpeech: A Speech Dialogue Interaction Corpus for Spoken Dialogue Model** (Findings of EMNLP 2025). |
|
|
| InteractSpeech is designed for real-time spoken-dialogue interaction, including interruptions, backchannels, pauses, gaps, overlaps, and turn transitions. The paper describes an approximately 150-hour English corpus containing 90,000 text utterances. |
|
|
| ## Release contents |
|
|
| | Item | Value | |
| | --- | ---: | |
| | Training records | 59,020 | |
| | Parquet shards | 3 | |
| | Audio waveforms | Not included | |
| | Video assets | Not included | |
|
|
| The release preserves dialogue text, speaker timing, interaction events, overlap regions, complete interrupted utterances, quality metadata, and per-utterance TTS reconstruction specifications. Audio can be regenerated with any TTS system while preserving each published segment duration and the global dialogue timeline. |
|
|
| ## Data format |
|
|
| Each Parquet row contains: |
|
|
| | Column | Description | |
| | --- | --- | |
| | `id` | Public record identifier | |
| | `view` | Dataset view | |
| | `category` | Dataset category | |
| | `record_json` | Complete record serialized as JSON | |
|
|
| ```python |
| import json |
| import pyarrow.parquet as pq |
| |
| table = pq.read_table("data/train-00000.parquet", columns=["record_json"]) |
| sample = json.loads(table["record_json"][0].as_py()) |
| ``` |
|
|
| The decoded record contains: |
|
|
| - `turns`: the training dialogue with timestamps and input-speech reconstruction recipes; |
| - `meta`: dialogue-level language, topic, event counts, and truncation information; |
| - `source_record`: the preserved InteractionSpeech dialogue, event annotations, full utterances, overlap regions, and source-to-training turn mapping. |
|
|
| For each input-speech segment, synthesize the text in `audio_in.source.transcript` using a consistent voice for the same `voice_key`, then fit it to `reference_duration_ms`. Do not alter the published global `start_ms` and `end_ms` values. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{chen-etal-2025-interactspeech, |
| title = {{I}nteract{S}peech: A Speech Dialogue Interaction Corpus for Spoken Dialogue Model}, |
| author = {Chen, Yifu and Ji, Shengpeng and Wang, Ziqing and Wang, Hanting and Zhao, Zhou}, |
| booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2025}, |
| month = nov, |
| year = {2025}, |
| address = {Suzhou, China}, |
| publisher = {Association for Computational Linguistics}, |
| pages = {8024--8033}, |
| doi = {10.18653/v1/2025.findings-emnlp.424}, |
| url = {https://aclanthology.org/2025.findings-emnlp.424/} |
| } |
| ``` |
|
|
| - Paper: https://aclanthology.org/2025.findings-emnlp.424/ |
| - Project page and audio examples: https://interactspeech.github.io/ |
|
|
