mcamara/all-words-in-english-with-pink-trombone

Dataset Card for Pink Trombone English Phonetic & Landmark Dataset

Repository: mcamara/all-words-in-english-with-pink-trombone
Modality: Audio + Time-aligned Events (landmarks) + Articulatory keyframes
Language: English (IPA)
Sampling rate: 48,000 Hz (mono)
Size: 115,487 items (single split: train)
Indexing: Alphabetical by id (orthographic word)

---

1) Summary

A large-scale, clean synthetic speech dataset generated with the Pink Trombone articulatory synthesizer.
Each example links a word → (i) audio, (ii) a phoneme/keyframe script used for synthesis, and (iii) acoustic landmarks extracted from the model’s internal state. Landmark types follow a Stevens-style inventory (e.g., stop closure/release, fricative onset/release, Vowel, Glide).

Primary use: training and evaluating acoustic landmark detection.
Secondary uses: phoneme recognition, articulatory–acoustic modeling, TTS/control experiments, data augmentation.

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2) What’s in each example?

• id (string): the word (e.g., "bashore").
• audio (datasets.Audio): mono WAV @ 48 kHz.
• utterance (string, JSON-formatted): Pink Trombone keyframes (phoneme tags, timing, control parameters).
• landmarks (string, JSON-formatted): time-stamped events (type, time, name).

Note: utterance and landmarks are stored as JSON strings for portability. Parse them on load.

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3) Example instance (abridged)

{
  "id": "bashore",
  "audio": {
    "path": "bashore.wav",
    "array": "…",
    "sampling_rate": 48000
  },
  "utterance": "{\"name\":\"bashore\",\"keyframes\":[
    {\"isSubPhoneme\":false,\"intensity\":1,\"name\":\"b(0)\",
     \"frontConstriction.diameter\":0.088, \"backConstriction.diameter\":5,
     \"tenseness\":0.691, \"loudness\":0.912, \"frontConstriction.index\":41.108,
     \"time\":0.10, \"frequency\":140, \"tractLength\":44},

    {\"isSubPhoneme\":false,\"name\":\"b(0)]\",\"isHold\":true,\"time\":0.15},
    {\"isSubPhoneme\":true, \"name\":\"b(1)\",  \"time\":0.16},
    {\"isSubPhoneme\":true, \"name\":\"b(1)]\",\"time\":0.17},

    {\"isSubPhoneme\":false,\"name\":\"æ\",\"tongue.index\":14.007,
     \"tongue.diameter\":2.887, \"time\":0.27},

    {\"isSubPhoneme\":false,\"name\":\"ʃ\",\"frontConstriction.index\":31.583,
     \"tongue.index\":38.116,\"tongue.diameter\":4.172, \"time\":0.37},

    {\"isSubPhoneme\":false,\"name\":\"ɚ\",\"frontConstriction.index\":28.317,
     \"tongue.index\":8.941, \"tongue.diameter\":1.365, \"time\":0.522}
  ]}",
  "landmarks": "[{\"type\":\"Sc\",\"time\":0.10,\"name\":\"b(0)\"},
                 {\"type\":\"Sr\",\"time\":0.155,\"name\":\"transition\"},
                 {\"type\":\"V\", \"time\":0.27, \"name\":\"æ\"},
                 {\"type\":\"Fc\",\"time\":0.37, \"name\":\"ʃ\"},
                 {\"type\":\"Fr\",\"time\":0.422,\"name\":\"ʃ]}\"},
                 {\"type\":\"V\", \"time\":0.522,\"name\":\"ɚ\"}]"
}

A smaller “toy” word:

{
  "id": "basic",
  "utterance": "{\"name\":\"basic\",\"keyframes\":[
    {\"name\":\"b\", \"time\":0.10, \"isSubPhoneme\":false},
    {\"name\":\"eɪ\",\"time\":0.22, \"isSubPhoneme\":false},
    {\"name\":\"s\",  \"time\":0.34, \"isSubPhoneme\":false},
    {\"name\":\"ɪ\",  \"time\":0.46, \"isSubPhoneme\":false},
    {\"name\":\"k\",  \"time\":0.58, \"isSubPhoneme\":false}
  ]}",
  "landmarks": "[{\"type\":\"Sc\",\"time\":0.10,\"name\":\"b\"},
                 {\"type\":\"Sr\",\"time\":0.16,\"name\":\"b\"},
                 {\"type\":\"V\", \"time\":0.22,\"name\":\"eɪ\"}]"
}

---

4) Landmark taxonomy

Landmarks are instantaneous events (times in seconds from audio start):

Code	Meaning (intuition)	Typical trigger
Sc	Stop closure	Oral constriction reaches closure
Sr	Stop release (burst/VOT onset)	Closure releases / pressure burst
Fc	Fricative onset	Narrow constriction → turbulence onset
Fr	Fricative release	Turbulence ceases
V	Vowel event (steady vocalic segment)	Stable vocalic target (F1–F2 region)
Nc	Nasal closure	Closure of nasal cavity
Nr	Nasal release	Release of nasal cavity
G	Glide	Vocalic Narrow constriction

The inventory is inspired by Stevens’ acoustic landmark theory. Exact emission is derived from Pink Trombone’s internal state machine and target transitions.

---

5) Utterance/keyframe controls (Pink Trombone)

Common fields observed in utterance.keyframes:

• Timing and tags: name (phoneme or sub-phoneme like b(0), b(1)), time (s), isSubPhoneme, isHold, isSilent, intensity, intensityMultiplier.

• Articulators / tract:

  • tongue.index, tongue.diameter
  • frontConstriction.index, frontConstriction.diameter
  • backConstriction.diameter
  • tractLength

• Source / prosody: tenseness (voicing), frequency (F0, Hz), loudness.

These are targets at specific times; the synthesizer interpolates between them to generate continuous motion and audio.

---

6) Loading and parsing

from datasets import load_dataset
import json

ds = load_dataset("mcamara/all-words-in-english-with-pink-trombone", split="train")

ex = ds[0]
audio = ex["audio"]                      # datasets.Audio -> numpy array + sr
utt = json.loads(ex["utterance"])        # dict, contains "name" + "keyframes"
lms = json.loads(ex["landmarks"])        # list of {type, time, name}

# Example: convert landmarks to a frame-level label vector (10 ms hop)
import numpy as np

sr = audio["sampling_rate"]
hop = int(0.01 * sr)  # 10 ms
n_frames = int(np.ceil(len(audio["array"]) / hop))
frame_times = np.arange(n_frames) * (hop / sr)

# For each frame, list all landmark types within ±15 ms
tol = 0.015
per_frame_labels = [
    [e["type"] for e in lms if abs(e["time"] - t) <= tol]
    for t in frame_times
]

---

7) Recommended evaluation protocols (landmark detection)

• Event-matching tolerance: ±10–20 ms around reference time. Report precision / recall / F1 by class and macro-averaged.
• Class subset: report both full set and obstruent-only (Sc/Sr/Fc/Fr) subsets.
• Metrics: Average Precision (AP) per class optional; DET curves for Sc/Sr.

Baseline idea (sketch):

• Input: 80-bin log-mel spectrogram (25 ms window, 10 ms hop).
• Model: small CRNN or 1D-TCN predicting event probabilities per frame (multi-label).
• Decoding: local-peak picking + NMS with min separation (e.g., 30 ms) to emit events.

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8) Suggested use-cases

• Acoustic Landmark Detection (primary).
• ASR pretraining / weak supervision: align synthetic landmarks with human corpora.
• Articulatory–acoustic modeling: learn mappings from keyframes ↔ acoustics.
• TTS/control: use keyframes as interpretable conditioning signals.
• Curricula / pedagogy: visualize gesture → acoustics with perfect alignments.

---

9) Dataset structure and splits

• Split: single train covering 115,487 English words (alphabetically keyed by id).
• One token per word (canonical, isolated pronunciation).
• To create eval sets, we recommend deterministic sampling by word hash (e.g., 90/5/5 train/dev/test) to maintain reproducibility and lexical balance.

import hashlib

def bucket(word, K=1000):
    return int(hashlib.md5(word.encode()).hexdigest(), 16) % K

# Example: split by bucket
train_idx = [i for i, ex in enumerate(ds) if bucket(ex["id"]) < 900]      # 90%
dev_idx   = [i for i, ex in enumerate(ds) if 900 <= bucket(ex["id"]) < 950]# 5%
test_idx  = [i for i, ex in enumerate(ds) if bucket(ex["id"]) >= 950]      # 5%

---

10) Data fields (schema)

Field	Type	Description
id	string	Orthographic word, unique key.
audio	datasets.Audio	Mono 48 kHz waveform.
utterance	string (JSON)	Synthesis plan (word name + keyframe list).
landmarks	string (JSON)	Time-ordered acoustic events.

utterance JSON

{
  "name": "<word>",
  "keyframes": [
    {
      "name": "<phoneme or tag>",
      "time": <float seconds>,
      "isSubPhoneme": <bool>,
      "isHold": <bool>,
      "isSilent": <bool>,
      "intensity": <float>,
      "intensityMultiplier": <float>,
      "tongue.index": <float>,
      "tongue.diameter": <float>,
      "frontConstriction.index": <float>,
      "frontConstriction.diameter": <float>,
      "backConstriction.diameter": <float>,
      "tenseness": <float>,
      "loudness": <float>,
      "tractLength": <float>,
      "frequency": <float Hz>
    }
  ]
}

landmarks JSON

[
  { "type": "Sc|Sr|Fc|Fr|V|Gc|Gr", "time": <float seconds>, "name": "<tag>" }
]

---

11) Generation process (high-level)

1. Canonical IPA per word (dictionary-style pronunciation).
2. Manual articulatory mapping → Pink Trombone control targets for each phoneme; sub-phoneme tags for closures/releases where relevant.
3. Synthesis at 48 kHz (mono).
4. Event extraction from the model state to emit landmarks aligned to the audio and keyframe times.
5. Packaging into HF dataset: audio + JSON strings (utterance, landmarks) per item.

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12) Quality checks

• Consistency: each example has audio, a non-empty keyframe list, and at least one landmark.
• Alphabetical keys: id sorted to simplify indexing and sharding.
• Spot-checks: vowel targets near expected F1–F2 regions; sibilant spectral centroid; stop bursts visible in spectrograms near Sr.

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13) Considerations & limitations

• Synthetic single-voice (Pink Trombone): no inter-speaker variability.
• Canonical forms (isolated pronunciations): limited coarticulation diversity.
• Model-specific parameters: Pink Trombone naming/ranges; not directly comparable to other articulatory models without mapping.

Mitigations: augment with noise/reverberation; mix with human corpora; randomize tract parameters moderately for robustness studies.

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14) How to visualize

# Quick plot of waveform + landmark stems (matplotlib)
import matplotlib.pyplot as plt
import numpy as np, json

y = audio["array"]; sr = audio["sampling_rate"]
t = np.arange(len(y))/sr
lms = json.loads(ex["landmarks"])

plt.figure()
plt.plot(t, y)
for e in lms:
    plt.axvline(e["time"], linestyle="--", alpha=0.4)
plt.title(f'{ex["id"]} – landmarks: {sorted(set([e["type"] for e in lms]))}')
plt.xlabel("Time (s)"); plt.ylabel("Amplitude")
plt.show()

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15) Ethics / intended use

• Intended for research and education in speech/phonetics/ML.
• Not human speech; do not use as-is for biometric or speaker ID tasks.
• If mixing with human data, follow the human corpus license and ethics guidelines.

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16) Citation

Please cite both the landmark theory and this dataset:

@book{stevens1998acoustic,
  title={Acoustic Phonetics},
  author={Stevens, Kenneth N.},
  year={1998},
  publisher={MIT Press}
}
@misc{pink_trombone_english_landmarks,
  author = {Cámara, Mateo},
  title  = {Pink Trombone English Phonetic \& Landmark Dataset},
  year   = {2025},
  howpublished = {\url{https://huggingface.co/datasets/mcamara/all-words-in-english-with-pink-trombone}}
}