---
license: mit
task_categories:
  - automatic-speech-recognition
  - text-to-speech
language:
  - en
tags:
  - speech
  - phonetics
  - articulatory
  - landmarks
  - vocaltractlab
  - synthesis
size_categories:
  - 100K<n<1M
---

# VTL Speech Landmarks Dataset

Articulatory speech synthesis dataset with acoustic landmarks, generated using VocalTractLab (VTL).

## Dataset Description

This dataset contains synthesized speech for **117,497 English words** from the CMU Pronouncing Dictionary, generated with two speakers (male and female). Each word includes:

- **Audio**: 48kHz WAV files
- **Landmarks**: Acoustic-phonetic event markers (JSON)
- **Articulatory data**: Full vocal tract trajectories from VTL (JSON)

### Speakers

| Speaker | Base F0 | Emphasis |
|---------|---------|----------|
| Male    | 120 Hz  | 1.2      |
| Female  | 200 Hz  | 1.4      |

## Dataset Structure

```
vtl-speech-landmarks/
├── male/
│   ├── wav/                     # 117,497 audio files
│   ├── landmarks/               # 117,497 landmark JSON files
│   └── articulatory/            # 117,497 articulatory JSON files
└── female/
    ├── wav/                     # 117,497 audio files
    ├── landmarks/               # 117,497 landmark JSON files
    └── articulatory/            # 117,497 articulatory JSON files
```

## Landmark Types

| Type | Name | Description |
|------|------|-------------|
| **V** | Vowel | Maximum mid-frequency energy |
| **G** | Glide | Energy change in formant region (for r, l, w, y) |
| **Sc** | Stop Closure | Start of oral closure |
| **Sr** | Stop Release | Burst energy when stop releases |
| **Fc** | Fricative Closure | Peak frication turbulence onset |
| **Fr** | Fricative Release | Transition out of fricative |
| **Nc** | Nasal Closure | Abrupt change when nasal begins |
| **Nr** | Nasal Release | Abrupt change when nasal ends |

## File Formats

### Landmarks JSON (`*_landmarks.json`)

```json
{
  "word": "hello",
  "pronunciation": "HH AH0 L OW1",
  "arpabet": ["HH", "AH0", "L", "OW1"],
  "vtl_phonemes": ["h", "@", "l", "O", "U"],
  "speaker": "male",
  "duration_ms": 520.0,
  "sample_rate": 48000,
  "landmarks": [
    {"type": "Fc", "time_ms": 50.0, "phoneme": "h", "ipa": "h", "confidence": 0.95},
    {"type": "V", "time_ms": 180.0, "phoneme": "@", "ipa": "ʌ", "confidence": 0.92},
    ...
  ],
  "phoneme_timings": [
    {"phoneme": "h", "start": 0.05, "end": 0.12, "duration": 0.07},
    ...
  ]
}
```

### Articulatory JSON (`*_articulatory.json`)

```json
{
  "sample_rate": 400.0,
  "num_frames": 208,
  "time": [0.0, 0.0025, 0.005, ...],
  "velum_opening": [...],
  "lip_aperture": [...],
  "tongue_tip_constriction": [...],
  "tongue_body_constriction": [...],
  "tube_areas": [[...], [...], ...]
}
```

### Audio (WAV)

- Format: 16-bit PCM
- Sample rate: 48,000 Hz
- Channels: Mono

## Usage

### Download specific files

```python
from huggingface_hub import hf_hub_download

# Download a landmark file
landmarks_path = hf_hub_download(
    repo_id="mcamara/vtl-speech-landmarks",
    filename="male/landmarks/hello_landmarks.json",
    repo_type="dataset"
)

# Download audio
audio_path = hf_hub_download(
    repo_id="mcamara/vtl-speech-landmarks",
    filename="male/wav/hello.wav",
    repo_type="dataset"
)
```

### Load landmarks

```python
import json

with open(landmarks_path) as f:
    data = json.load(f)

print(f"Word: {data['word']}")
print(f"Duration: {data['duration_ms']} ms")
for lm in data['landmarks']:
    print(f"  {lm['type']} at {lm['time_ms']:.1f} ms - {lm['phoneme']} ({lm['ipa']})")
```

### Load and play audio

```python
import soundfile as sf

audio, sr = sf.read(audio_path)
print(f"Sample rate: {sr}, Duration: {len(audio)/sr:.2f}s")
```

## Generation Details

- **Source**: CMU Pronouncing Dictionary (117,497 words)
- **Synthesizer**: VocalTractLab (VTL) articulatory speech synthesizer
- **Landmark extraction**: Energy-based detection from spectral analysis
- **Phoneme mapping**: ARPABET to VTL phoneme conversion with diphthong expansion

## Applications

- Acoustic-phonetic research
- Speech recognition training data
- Text-to-speech development
- Phonetic landmark detection models
- Articulatory synthesis research

## Citation

If you use this dataset, please cite:

```bibtex
@misc{vtl-speech-landmarks,
  title={VTL Speech Landmarks Dataset},
  year={2025},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/datasets/mcamara/vtl-speech-landmarks}}
}
```

## License

MIT License