README.md

---
license: apache-2.0
task_categories:
- audio-classification
language:
- en
tags:
- speech
size_categories:
- 10B<n<100B
---
# MISP-QEKS: A Large-Scale Tri-Modal Benchmark for Query-by-Example Keyword Spotting

Official dataset release for:

**MISP-QEKS: A Large-Scale Dataset with Multimodal Cues for Query-by-Example Keyword Spotting**  
ACM MM 2025, Dublin, Ireland  
DOI: https://doi.org/10.1145/3746027.3758268  

---

## Overview

MISP-QEKS is the first large-scale tri-modal benchmark for open-vocabulary Query-by-Example Keyword Spotting (QEKS).

Unlike traditional keyword spotting datasets that:

- focus on fixed keyword sets
- rely on clean audio-only recordings
- lack OOV evaluation

MISP-QEKS provides:

- Fully aligned Text–Audio–Visual keyword clips
- Real-world noise simulation
- In-Vocabulary (IV) and Out-of-Vocabulary (OOV) evaluation splits
- 610,000 enrollment–query pairs
- 9,830+ distinct keywords

This dataset enables robust, multimodal, open-vocabulary keyword spotting research under realistic acoustic conditions.

---

## Task Definition

![Tri-modal QEKS Framework](fig/tri_modal.jpg)

As shown in the figure, given:

- An enrollment example (text/audio/video)
- A query clip (audio/video)

The system predicts:

- Whether both samples contain the same keyword
- A probability score

This supports:

- Open-vocabulary keyword spotting
- Cross-modal matching
- Robust detection under noise

---

## Dataset Construction Pipeline

![Construction Pipeline](fig/task_dataset.jpg)

As shown in the figure, MISP-QEKS is constructed from sentence-level audio-visual-text data via:

1. Phone-level forced alignment
2. Word-level cropping
3. Real-world noise simulation
4. Enrollment–query pair construction

This pipeline enables large-scale, synchronized multimodal keyword samples suitable for open-vocabulary QEKS research.

## Dataset Statistics

- Total duration: **193.606 hours**
- Keywords: **9,830**
- Enrollment–Query pairs: **610,000**
  - 122,000 positive
  - 488,000 negative
- Positive:Negative ratio = 1:4

---

### Data Splits

| Split       | Duration (h) | Keywords | Pairs  | Positive | Negative |
|------------|--------------|----------|--------|----------|----------|
| Train      | 157.756      | 8,357    | 500,000 | 100,000  | 400,000  |
| Dev        | 3.245        | 2,247    | 10,000  | 2,000    | 8,000    |
| Eval-seen  | 15.300       | 2,174    | 50,000  | 10,000   | 40,000   |
| Eval-blind | 17.305       | 1,445    | 50,000  | 10,000   | 40,000   |

Evaluation protocol:

- Eval-seen → In-Vocabulary (IV)
- Eval-blind → Out-of-Vocabulary (OOV)
- Speaker-independent split

---

### Keyword Frequency Distribution

![Keyword Frequency Histogram](fig/histogram-frequency.jpg)

The keyword frequency distribution demonstrates:

- Strong coverage across high-frequency and mid-frequency words.
- Long-tail behavior suitable for evaluating generalization.

This design supports robust training while preserving realistic lexical imbalance.

---

## Noise Characteristics and Quality Distribution

To emulate realistic acoustic environments, clean clips are mixed with real-world background noise at SNR levels {+5, 0, −5, −10} dB.

### Speech Quality Distribution

![PESQ and STOI Distribution](fig/histogram.jpg)

- Most PESQ scores lie between 1.5 and 2.5.
- Most STOI values fall between 65% and 85%.

This confirms that the dataset spans a broad spectrum of realistic noise conditions.

---

## Repository Structure and File Description

The repository contains the following files:

### Data Archives

- [`train.zip`](./train.zip) 
  Contains the training split (157.756 hours, 500,000 enrollment–query pairs across 8,357 keywords).

- [`dev_seen.zip`](./dev_seen.zip)
  Development split for hyperparameter tuning on In-Vocabulary (IV) keywords.  
  Keywords partially overlap with the training set.

- [`dev_unseen.zip`](./dev_unseen.zip) 
  Development split for Out-of-Vocabulary (OOV) validation.  
  Keywords do not appear in the training set.

- [`eval_seen.zip`](./eval_seen.zip) 
  In-Vocabulary (IV) evaluation split.  
  Keywords appear in the training set and are used for standard evaluation.

- [`eval_unseen.zip`](./eval_unseen.zip)   
  Out-of-Vocabulary (OOV) evaluation split.  
  Keywords are not seen during training and are used to assess generalization.

---

### Noise and Metadata

- [`noise.zip`](./noise.zip)  
  Real-world background noise recordings used for acoustic simulation.

- [`snr_map.zip`](./snr_map.zip) 
  Mapping file indicating the signal-to-noise ratio (SNR) assigned to each noisy sample.

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### Baseline Checkpoint

- [`train/model/`](./train/model/)  

  Contains the official 10-epoch checkpoint of the XEQ-Matcher baseline described in the ACM MM 2025 paper.

  This checkpoint can be used directly for evaluation or reproduction of reported results.

  Official implementation:
  https://github.com/coalboss/MISP-QEKS

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### Pretrained Feature Extractors

- [`model/`](./model/)  

  Contains pretrained feature extraction models required by the baseline system.

  These models are used as frozen encoders for:

  - Audio feature extraction (e.g., Whisper-Tiny encoder)
  - Visual feature extraction (CNN-ResNet backbone)
  - Text processing (G2P model)

  These components are necessary to reproduce the reported baseline performance.