README.md

Semantic Interruption Detection Benchmark

A multilingual benchmark for evaluating semantic interruption detection in conversational speech. The dataset contains annotated audio recordings in English and Mandarin Chinese, along with a silence/noise negative set, designed to test models that decide when (and whether) to interrupt a speaker.

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Overview

In spoken dialogue systems and voice assistants, knowing when to interrupt a speaker — and detecting interruption signals in real time — is a critical yet under-studied task. This benchmark provides:

• 3,700 audio clips with frame-level interruption timestamps
• Bilingual coverage: English and Mandarin Chinese conversational speech
• Negative samples: 500 silence and environmental noise clips for robustness evaluation
• Three standardized metrics: FIR, IRL, and APT (defined below)

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Dataset Statistics

Split	Language	Total Samples	With Interruption	Without Interruption
en_test_lines.jsonl	English	1,600	1,100 (68.8%)	500 (31.3%)
zh_test_lines.jsonl	Chinese	1,600	1,100 (68.8%)	500 (31.3%)
silence_noise_test.jsonl	—	500	0 (0%)	500 (100%)
Total		3,700	2,200	1,500

Audio duration:

• English: 0.36s – 22.0s (avg 3.74s)
• Chinese: similar range (avg 3.85s)
• Silence/noise: 0.09s – 182.0s (avg 13.0s)

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Directory Structure

Semantic_Interaption/
├── README.md
├── test.ipynb                        # Evaluation notebook
└── test_wavs/
    ├── en_test_lines.jsonl           # English ground-truth annotations
    ├── zh_test_lines.jsonl           # Chinese ground-truth annotations
    ├── silence_noise_test.jsonl      # Silence / noise annotations
    ├── en/                           # English WAV files (1,600)
    ├── zh/                           # Chinese WAV files (1,600)
    └── silence_or_noise/             # Silence / noise WAV files (500)

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Annotation Format

Each .jsonl file contains one JSON object per line.

Speech splits (en_test_lines.jsonl, zh_test_lines.jsonl)

{
  "audio":           "MDT_ASR_AI106_A0016_S0005_0_G3265_0318890-0324670.wav",
  "total_nonbreak":  false,
  "duration":        5.78,
  "break_time":      0.125,
  "text_with_break": "<break> Experiencing different cultures, learning new languages, ..."
}

Field	Type	Description
audio	str	Filename of the corresponding WAV file
total_nonbreak	bool	true if no interruption occurs in the entire clip
duration	float	Total audio duration in seconds
break_time	float	Timestamp (seconds) of the first interruption signal; -1 when total_nonbreak=true
text_with_break	str	Transcript with <break> token inserted at the interruption point

Silence / noise split (silence_noise_test.jsonl)

All samples have total_nonbreak=true and text_with_break=null. These clips contain no speech and serve as a robustness / false-alarm test.

{
  "audio":           "wind_wind_heavy_clean_stdy_air.wav",
  "total_nonbreak":  true,
  "duration":        152.52,
  "break_time":      152.52,
  "text_with_break": null
}

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Evaluation Metrics

The benchmark defines three complementary metrics. Reference implementation is in test.ipynb.

FIR — False Interruption Rate

The fraction of non-interruption samples for which the model incorrectly predicts an interruption.

FIR = |false positives| / |total_nonbreak samples|

Lower is better.

IRL — Interruption Response Latency

Average absolute timing error (in seconds) over true-positive detections (model correctly identifies a break and the timestamp is within 50 ms of ground truth).

IRL = mean(|predicted_break_time - gt_break_time|)   [over true positives]

Lower is better.

APT — Average Penalty Time

A unified penalty that captures both detection errors and timing inaccuracies:

Prediction vs. Ground Truth	Penalty
Both predict no break	0
Predicted time within ±50 ms of ground truth	0
False alarm (predict break, no GT break)	Full clip duration
Missed detection (no prediction, GT has break)	duration − break_time
Late detection	predicted_time − break_time

APT = mean(penalty)   [over all samples]

Lower is better.

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Download

The full dataset (audio + annotations) is hosted on Hugging Face:

https://huggingface.co/datasets/kxxia/SID-bench

# Option 1: huggingface_hub CLI
huggingface-cli download kxxia/SID-bench --repo-type dataset --local-dir ./SID-bench

# Option 2: Python
from huggingface_hub import snapshot_download
snapshot_download(repo_id="kxxia/SID-bench", repo_type="dataset", local_dir="./SID-bench")

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Quick Start

import json

# Load ground-truth annotations
with open("test_wavs/en_test_lines.jsonl") as f:
    en_samples = [json.loads(line) for line in f]

# Each sample
sample = en_samples[0]
print(sample["audio"])           # WAV filename
print(sample["total_nonbreak"])  # bool
print(sample["break_time"])      # float (seconds)
print(sample["text_with_break"]) # transcript with <break> marker

Run test.ipynb to reproduce the evaluation pipeline and compute FIR / IRL / APT against your own model predictions.

Prediction format

Your model should produce a JSONL file with one prediction per line:

{"audio": "MDT_ASR_AI106_A0016_S0005_0_G3265_0318890-0324670.wav", "total_nonbreak": false, "break_time": 0.13}

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Use Cases

• Conversational AI / dialogue systems: training models to detect natural turn-taking signals
• Voice assistants: knowing when a user intends to interrupt or be interrupted
• Speech-to-speech translation: preserving interruption timing across languages
• Contact center analytics: segmenting overlapping speech

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License

Please check the LICENSE file for usage terms. The audio data may originate from multiple source corpora; refer to their respective licenses before use in commercial applications.

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Acknowledgement

The English conversational speech data used in this benchmark were derived from the English Duplex Conversation Training Dataset provided by MagicData.

MagicData. (2025). MDT-AF069 English Duplex Conversation Training Dataset. https://www.magicdatatech.com/datasets/asr/mdt-af069-multi-stream-spontaneous-conversation-training-datasets-english-1733294791

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Citation

If you use this benchmark in your research, please cite:

@misc{semantic_interruption_benchmark,
      title={Semantic-Aware Interruption Detection in Spoken Dialogue Systems: Benchmark, Metric, and Model}, 
      author={Kangxiang Xia and Bingshen Mu and Xian Shi and Jin Xu and Lei Xie},
      year={2026},
      eprint={2603.24144},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2603.24144}, 
}