Datasets:

1f
/

interactspeech

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

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

@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/